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+ + +'+ escapeHtml(title) + '
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+ Welcome to segpy's documentation!
+Segpy is a comprehensive pipeline designed for segregation analysis. This documentation provides a step-by-step tutorial on executing segregation analysis in a High-Performance Computing (HPC) environment utilizing the Slurm workload manager system (https://slurm.schedmd.com/), on a Linux workstation, or directly through the segpy module in Python. Segregation analysis is a crucial process for exploring genetic variants within a sample of sequence data. This pipeline facilitates the counting of affected and non-affected individuals with variants, including homozygous variants, those with no variants, and those with no calls. These counts are computed both within families and globally. Additionally, the pipeline offers a detailed breakdown not only of variants but also of alleles in each case. To execute segregation analysis successfully, it is imperative to have a pedigree file with six columns: familyid, individualid, parentalid, maternalid, sex{1:male; 2:female, 0:unknown}, and phenotype={1: control (unaffected), 2: proband(affected), -9:missing}. The genetic data must be provided in the vcf format.
For guidance on how to use segpy's pipeline, consult the tutorial.
+Contents
+-
+
- Installation +
- Tutorial: + +
- FAQ +
- Reference +
+
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+ Next »
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diff --git a/0.0.1/installation/index.html b/0.0.1/installation/index.html
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+ Installation
+I am HERE.
+segpy is a Python module specifically designed for segregation analysis, developed on Python 3.10.2. The module can be easily downloaded using pip package manager:
pip install 'git+https://github.com/neurobioinfo/segpy#subdirectory=segpy'
+Segregation analysis can be conducted utilizing the segpy scheduler, segpy.pip. If you have access to an HPC or Linux workstation, you can automate the process using segpy.pip. The scheduler script is written in Bash, making it compatible with systems such as Slurm or a Linux workstation.
wget https://github.com/neurobioinfo/segpy/releases/download/v0.2.2.3/segpy.pip.zip
+unzip segpy.pip.zip
+To obtain a brief overview of the pipeline, run the following code.
+bash ./segpy.pip/launch_segpy.sh -h
+
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diff --git a/0.0.1/js/html5shiv.min.js b/0.0.1/js/html5shiv.min.js
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+/**
+* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="
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+ Adjustable execution parameters for the segregation pipeline
+ ++
Introduction
+The configs/ directory contains the segpy.config.ini file which allows users to specify the parameters of interest explained below
+Segpy parameters
+ACCOUNT Specify user name for slurm module.
+SPARK_PATH Environment variables to point to the correct Spark executable
+ENV_PATH Environment variables to point to the correct Python executable
+PYTHON_CMD=python3.10
+MODULEUSE The path to the environmental module
+JAVA_CMD=java
+JAVA_VERSION=11.0.2
Specify the version of Java
+NCOL=7 To control usage of memory to process CSQ.
+CSQ=True To process CSQ in the output, Consequence annotations from Ensembl VEP.
+GRCH=GRCh38 Genome Reference Consortium Human
+SPARKMEM="16g" Spark Memory
+JAVATOOLOPTIONS="-Xmx8g" Java Memory and any other options can be added to here.
[step 1] Create table matrix
+WALLTIME_ARRAY["step_1"]=00-01:00 Set a limit on the total run time of the job allocation in the step 1 under slurm.
+THREADS_ARRAY["step_1"]=4 Request the maximum ntasks be invoked on each core in the step 1 under slurm.
+MEM_ARRAY["step_1"]=25g Specify the real memory required per node in the step 1 under slurm.
[step 2] Run segregation
+WALLTIME_ARRAY["step_2"]=00-02:00 Set a limit on the total run time of the job allocation in the step 2 under slurm.
+THREADS_ARRAY["step_2"]=4 Request the maximum ntasks be invoked on each core in the step 2 under slurm.
+MEM_ARRAY["step_2"]=17g Specify the real memory required per node in the step 2 under slurm.
+INFO_REQUIRED=[1,2,3,4] One can determine the desired content to be included in the output
+ 1: Global affected, 2: Global unaffected, 3: family-wise, 4: phenotype-family-wise, 5: phenotype-family-wise and non-include, 6: phenotype-family-wise-multipe sample, 7: phenotype-family-wise-multipe sample and non-include
[step 3] Parsing
+WALLTIME_ARRAY["step_3"]=00-01:00 Set a limit on the total run time of the job allocation in the step 3 under slurm.
+THREADS_ARRAY["step_3"]=4 Request the maximum ntasks be invoked on each core in the step 3 under slurm.
+MEM_ARRAY["step_3"]=4g Specify the real memory required per node in the step 3 under slurm.
+
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+ « Previous
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diff --git a/0.0.1/search.html b/0.0.1/search.html
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+ Search Results
+ + + +
+ Searching...
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diff --git a/0.0.1/search/lunr.js b/0.0.1/search/lunr.js
new file mode 100644
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+++ b/0.0.1/search/lunr.js
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+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
+ */
+
+;(function(){
+
+/**
+ * A convenience function for configuring and constructing
+ * a new lunr Index.
+ *
+ * A lunr.Builder instance is created and the pipeline setup
+ * with a trimmer, stop word filter and stemmer.
+ *
+ * This builder object is yielded to the configuration function
+ * that is passed as a parameter, allowing the list of fields
+ * and other builder parameters to be customised.
+ *
+ * All documents _must_ be added within the passed config function.
+ *
+ * @example
+ * var idx = lunr(function () {
+ * this.field('title')
+ * this.field('body')
+ * this.ref('id')
+ *
+ * documents.forEach(function (doc) {
+ * this.add(doc)
+ * }, this)
+ * })
+ *
+ * @see {@link lunr.Builder}
+ * @see {@link lunr.Pipeline}
+ * @see {@link lunr.trimmer}
+ * @see {@link lunr.stopWordFilter}
+ * @see {@link lunr.stemmer}
+ * @namespace {function} lunr
+ */
+var lunr = function (config) {
+ var builder = new lunr.Builder
+
+ builder.pipeline.add(
+ lunr.trimmer,
+ lunr.stopWordFilter,
+ lunr.stemmer
+ )
+
+ builder.searchPipeline.add(
+ lunr.stemmer
+ )
+
+ config.call(builder, builder)
+ return builder.build()
+}
+
+lunr.version = "2.3.9"
+/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A namespace containing utils for the rest of the lunr library
+ * @namespace lunr.utils
+ */
+lunr.utils = {}
+
+/**
+ * Print a warning message to the console.
+ *
+ * @param {String} message The message to be printed.
+ * @memberOf lunr.utils
+ * @function
+ */
+lunr.utils.warn = (function (global) {
+ /* eslint-disable no-console */
+ return function (message) {
+ if (global.console && console.warn) {
+ console.warn(message)
+ }
+ }
+ /* eslint-enable no-console */
+})(this)
+
+/**
+ * Convert an object to a string.
+ *
+ * In the case of `null` and `undefined` the function returns
+ * the empty string, in all other cases the result of calling
+ * `toString` on the passed object is returned.
+ *
+ * @param {Any} obj The object to convert to a string.
+ * @return {String} string representation of the passed object.
+ * @memberOf lunr.utils
+ */
+lunr.utils.asString = function (obj) {
+ if (obj === void 0 || obj === null) {
+ return ""
+ } else {
+ return obj.toString()
+ }
+}
+
+/**
+ * Clones an object.
+ *
+ * Will create a copy of an existing object such that any mutations
+ * on the copy cannot affect the original.
+ *
+ * Only shallow objects are supported, passing a nested object to this
+ * function will cause a TypeError.
+ *
+ * Objects with primitives, and arrays of primitives are supported.
+ *
+ * @param {Object} obj The object to clone.
+ * @return {Object} a clone of the passed object.
+ * @throws {TypeError} when a nested object is passed.
+ * @memberOf Utils
+ */
+lunr.utils.clone = function (obj) {
+ if (obj === null || obj === undefined) {
+ return obj
+ }
+
+ var clone = Object.create(null),
+ keys = Object.keys(obj)
+
+ for (var i = 0; i < keys.length; i++) {
+ var key = keys[i],
+ val = obj[key]
+
+ if (Array.isArray(val)) {
+ clone[key] = val.slice()
+ continue
+ }
+
+ if (typeof val === 'string' ||
+ typeof val === 'number' ||
+ typeof val === 'boolean') {
+ clone[key] = val
+ continue
+ }
+
+ throw new TypeError("clone is not deep and does not support nested objects")
+ }
+
+ return clone
+}
+lunr.FieldRef = function (docRef, fieldName, stringValue) {
+ this.docRef = docRef
+ this.fieldName = fieldName
+ this._stringValue = stringValue
+}
+
+lunr.FieldRef.joiner = "/"
+
+lunr.FieldRef.fromString = function (s) {
+ var n = s.indexOf(lunr.FieldRef.joiner)
+
+ if (n === -1) {
+ throw "malformed field ref string"
+ }
+
+ var fieldRef = s.slice(0, n),
+ docRef = s.slice(n + 1)
+
+ return new lunr.FieldRef (docRef, fieldRef, s)
+}
+
+lunr.FieldRef.prototype.toString = function () {
+ if (this._stringValue == undefined) {
+ this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef
+ }
+
+ return this._stringValue
+}
+/*!
+ * lunr.Set
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A lunr set.
+ *
+ * @constructor
+ */
+lunr.Set = function (elements) {
+ this.elements = Object.create(null)
+
+ if (elements) {
+ this.length = elements.length
+
+ for (var i = 0; i < this.length; i++) {
+ this.elements[elements[i]] = true
+ }
+ } else {
+ this.length = 0
+ }
+}
+
+/**
+ * A complete set that contains all elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.complete = {
+ intersect: function (other) {
+ return other
+ },
+
+ union: function () {
+ return this
+ },
+
+ contains: function () {
+ return true
+ }
+}
+
+/**
+ * An empty set that contains no elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.empty = {
+ intersect: function () {
+ return this
+ },
+
+ union: function (other) {
+ return other
+ },
+
+ contains: function () {
+ return false
+ }
+}
+
+/**
+ * Returns true if this set contains the specified object.
+ *
+ * @param {object} object - Object whose presence in this set is to be tested.
+ * @returns {boolean} - True if this set contains the specified object.
+ */
+lunr.Set.prototype.contains = function (object) {
+ return !!this.elements[object]
+}
+
+/**
+ * Returns a new set containing only the elements that are present in both
+ * this set and the specified set.
+ *
+ * @param {lunr.Set} other - set to intersect with this set.
+ * @returns {lunr.Set} a new set that is the intersection of this and the specified set.
+ */
+
+lunr.Set.prototype.intersect = function (other) {
+ var a, b, elements, intersection = []
+
+ if (other === lunr.Set.complete) {
+ return this
+ }
+
+ if (other === lunr.Set.empty) {
+ return other
+ }
+
+ if (this.length < other.length) {
+ a = this
+ b = other
+ } else {
+ a = other
+ b = this
+ }
+
+ elements = Object.keys(a.elements)
+
+ for (var i = 0; i < elements.length; i++) {
+ var element = elements[i]
+ if (element in b.elements) {
+ intersection.push(element)
+ }
+ }
+
+ return new lunr.Set (intersection)
+}
+
+/**
+ * Returns a new set combining the elements of this and the specified set.
+ *
+ * @param {lunr.Set} other - set to union with this set.
+ * @return {lunr.Set} a new set that is the union of this and the specified set.
+ */
+
+lunr.Set.prototype.union = function (other) {
+ if (other === lunr.Set.complete) {
+ return lunr.Set.complete
+ }
+
+ if (other === lunr.Set.empty) {
+ return this
+ }
+
+ return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))
+}
+/**
+ * A function to calculate the inverse document frequency for
+ * a posting. This is shared between the builder and the index
+ *
+ * @private
+ * @param {object} posting - The posting for a given term
+ * @param {number} documentCount - The total number of documents.
+ */
+lunr.idf = function (posting, documentCount) {
+ var documentsWithTerm = 0
+
+ for (var fieldName in posting) {
+ if (fieldName == '_index') continue // Ignore the term index, its not a field
+ documentsWithTerm += Object.keys(posting[fieldName]).length
+ }
+
+ var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)
+
+ return Math.log(1 + Math.abs(x))
+}
+
+/**
+ * A token wraps a string representation of a token
+ * as it is passed through the text processing pipeline.
+ *
+ * @constructor
+ * @param {string} [str=''] - The string token being wrapped.
+ * @param {object} [metadata={}] - Metadata associated with this token.
+ */
+lunr.Token = function (str, metadata) {
+ this.str = str || ""
+ this.metadata = metadata || {}
+}
+
+/**
+ * Returns the token string that is being wrapped by this object.
+ *
+ * @returns {string}
+ */
+lunr.Token.prototype.toString = function () {
+ return this.str
+}
+
+/**
+ * A token update function is used when updating or optionally
+ * when cloning a token.
+ *
+ * @callback lunr.Token~updateFunction
+ * @param {string} str - The string representation of the token.
+ * @param {Object} metadata - All metadata associated with this token.
+ */
+
+/**
+ * Applies the given function to the wrapped string token.
+ *
+ * @example
+ * token.update(function (str, metadata) {
+ * return str.toUpperCase()
+ * })
+ *
+ * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.update = function (fn) {
+ this.str = fn(this.str, this.metadata)
+ return this
+}
+
+/**
+ * Creates a clone of this token. Optionally a function can be
+ * applied to the cloned token.
+ *
+ * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.clone = function (fn) {
+ fn = fn || function (s) { return s }
+ return new lunr.Token (fn(this.str, this.metadata), this.metadata)
+}
+/*!
+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A function for splitting a string into tokens ready to be inserted into
+ * the search index. Uses `lunr.tokenizer.separator` to split strings, change
+ * the value of this property to change how strings are split into tokens.
+ *
+ * This tokenizer will convert its parameter to a string by calling `toString` and
+ * then will split this string on the character in `lunr.tokenizer.separator`.
+ * Arrays will have their elements converted to strings and wrapped in a lunr.Token.
+ *
+ * Optional metadata can be passed to the tokenizer, this metadata will be cloned and
+ * added as metadata to every token that is created from the object to be tokenized.
+ *
+ * @static
+ * @param {?(string|object|object[])} obj - The object to convert into tokens
+ * @param {?object} metadata - Optional metadata to associate with every token
+ * @returns {lunr.Token[]}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.tokenizer = function (obj, metadata) {
+ if (obj == null || obj == undefined) {
+ return []
+ }
+
+ if (Array.isArray(obj)) {
+ return obj.map(function (t) {
+ return new lunr.Token(
+ lunr.utils.asString(t).toLowerCase(),
+ lunr.utils.clone(metadata)
+ )
+ })
+ }
+
+ var str = obj.toString().toLowerCase(),
+ len = str.length,
+ tokens = []
+
+ for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {
+ var char = str.charAt(sliceEnd),
+ sliceLength = sliceEnd - sliceStart
+
+ if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {
+
+ if (sliceLength > 0) {
+ var tokenMetadata = lunr.utils.clone(metadata) || {}
+ tokenMetadata["position"] = [sliceStart, sliceLength]
+ tokenMetadata["index"] = tokens.length
+
+ tokens.push(
+ new lunr.Token (
+ str.slice(sliceStart, sliceEnd),
+ tokenMetadata
+ )
+ )
+ }
+
+ sliceStart = sliceEnd + 1
+ }
+
+ }
+
+ return tokens
+}
+
+/**
+ * The separator used to split a string into tokens. Override this property to change the behaviour of
+ * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.
+ *
+ * @static
+ * @see lunr.tokenizer
+ */
+lunr.tokenizer.separator = /[\s\-]+/
+/*!
+ * lunr.Pipeline
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.Pipelines maintain an ordered list of functions to be applied to all
+ * tokens in documents entering the search index and queries being ran against
+ * the index.
+ *
+ * An instance of lunr.Index created with the lunr shortcut will contain a
+ * pipeline with a stop word filter and an English language stemmer. Extra
+ * functions can be added before or after either of these functions or these
+ * default functions can be removed.
+ *
+ * When run the pipeline will call each function in turn, passing a token, the
+ * index of that token in the original list of all tokens and finally a list of
+ * all the original tokens.
+ *
+ * The output of functions in the pipeline will be passed to the next function
+ * in the pipeline. To exclude a token from entering the index the function
+ * should return undefined, the rest of the pipeline will not be called with
+ * this token.
+ *
+ * For serialisation of pipelines to work, all functions used in an instance of
+ * a pipeline should be registered with lunr.Pipeline. Registered functions can
+ * then be loaded. If trying to load a serialised pipeline that uses functions
+ * that are not registered an error will be thrown.
+ *
+ * If not planning on serialising the pipeline then registering pipeline functions
+ * is not necessary.
+ *
+ * @constructor
+ */
+lunr.Pipeline = function () {
+ this._stack = []
+}
+
+lunr.Pipeline.registeredFunctions = Object.create(null)
+
+/**
+ * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token
+ * string as well as all known metadata. A pipeline function can mutate the token string
+ * or mutate (or add) metadata for a given token.
+ *
+ * A pipeline function can indicate that the passed token should be discarded by returning
+ * null, undefined or an empty string. This token will not be passed to any downstream pipeline
+ * functions and will not be added to the index.
+ *
+ * Multiple tokens can be returned by returning an array of tokens. Each token will be passed
+ * to any downstream pipeline functions and all will returned tokens will be added to the index.
+ *
+ * Any number of pipeline functions may be chained together using a lunr.Pipeline.
+ *
+ * @interface lunr.PipelineFunction
+ * @param {lunr.Token} token - A token from the document being processed.
+ * @param {number} i - The index of this token in the complete list of tokens for this document/field.
+ * @param {lunr.Token[]} tokens - All tokens for this document/field.
+ * @returns {(?lunr.Token|lunr.Token[])}
+ */
+
+/**
+ * Register a function with the pipeline.
+ *
+ * Functions that are used in the pipeline should be registered if the pipeline
+ * needs to be serialised, or a serialised pipeline needs to be loaded.
+ *
+ * Registering a function does not add it to a pipeline, functions must still be
+ * added to instances of the pipeline for them to be used when running a pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @param {String} label - The label to register this function with
+ */
+lunr.Pipeline.registerFunction = function (fn, label) {
+ if (label in this.registeredFunctions) {
+ lunr.utils.warn('Overwriting existing registered function: ' + label)
+ }
+
+ fn.label = label
+ lunr.Pipeline.registeredFunctions[fn.label] = fn
+}
+
+/**
+ * Warns if the function is not registered as a Pipeline function.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @private
+ */
+lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {
+ var isRegistered = fn.label && (fn.label in this.registeredFunctions)
+
+ if (!isRegistered) {
+ lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn)
+ }
+}
+
+/**
+ * Loads a previously serialised pipeline.
+ *
+ * All functions to be loaded must already be registered with lunr.Pipeline.
+ * If any function from the serialised data has not been registered then an
+ * error will be thrown.
+ *
+ * @param {Object} serialised - The serialised pipeline to load.
+ * @returns {lunr.Pipeline}
+ */
+lunr.Pipeline.load = function (serialised) {
+ var pipeline = new lunr.Pipeline
+
+ serialised.forEach(function (fnName) {
+ var fn = lunr.Pipeline.registeredFunctions[fnName]
+
+ if (fn) {
+ pipeline.add(fn)
+ } else {
+ throw new Error('Cannot load unregistered function: ' + fnName)
+ }
+ })
+
+ return pipeline
+}
+
+/**
+ * Adds new functions to the end of the pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.
+ */
+lunr.Pipeline.prototype.add = function () {
+ var fns = Array.prototype.slice.call(arguments)
+
+ fns.forEach(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+ this._stack.push(fn)
+ }, this)
+}
+
+/**
+ * Adds a single function after a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.after = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ pos = pos + 1
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Adds a single function before a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.before = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Removes a function from the pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.
+ */
+lunr.Pipeline.prototype.remove = function (fn) {
+ var pos = this._stack.indexOf(fn)
+ if (pos == -1) {
+ return
+ }
+
+ this._stack.splice(pos, 1)
+}
+
+/**
+ * Runs the current list of functions that make up the pipeline against the
+ * passed tokens.
+ *
+ * @param {Array} tokens The tokens to run through the pipeline.
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.run = function (tokens) {
+ var stackLength = this._stack.length
+
+ for (var i = 0; i < stackLength; i++) {
+ var fn = this._stack[i]
+ var memo = []
+
+ for (var j = 0; j < tokens.length; j++) {
+ var result = fn(tokens[j], j, tokens)
+
+ if (result === null || result === void 0 || result === '') continue
+
+ if (Array.isArray(result)) {
+ for (var k = 0; k < result.length; k++) {
+ memo.push(result[k])
+ }
+ } else {
+ memo.push(result)
+ }
+ }
+
+ tokens = memo
+ }
+
+ return tokens
+}
+
+/**
+ * Convenience method for passing a string through a pipeline and getting
+ * strings out. This method takes care of wrapping the passed string in a
+ * token and mapping the resulting tokens back to strings.
+ *
+ * @param {string} str - The string to pass through the pipeline.
+ * @param {?object} metadata - Optional metadata to associate with the token
+ * passed to the pipeline.
+ * @returns {string[]}
+ */
+lunr.Pipeline.prototype.runString = function (str, metadata) {
+ var token = new lunr.Token (str, metadata)
+
+ return this.run([token]).map(function (t) {
+ return t.toString()
+ })
+}
+
+/**
+ * Resets the pipeline by removing any existing processors.
+ *
+ */
+lunr.Pipeline.prototype.reset = function () {
+ this._stack = []
+}
+
+/**
+ * Returns a representation of the pipeline ready for serialisation.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.toJSON = function () {
+ return this._stack.map(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+
+ return fn.label
+ })
+}
+/*!
+ * lunr.Vector
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A vector is used to construct the vector space of documents and queries. These
+ * vectors support operations to determine the similarity between two documents or
+ * a document and a query.
+ *
+ * Normally no parameters are required for initializing a vector, but in the case of
+ * loading a previously dumped vector the raw elements can be provided to the constructor.
+ *
+ * For performance reasons vectors are implemented with a flat array, where an elements
+ * index is immediately followed by its value. E.g. [index, value, index, value]. This
+ * allows the underlying array to be as sparse as possible and still offer decent
+ * performance when being used for vector calculations.
+ *
+ * @constructor
+ * @param {Number[]} [elements] - The flat list of element index and element value pairs.
+ */
+lunr.Vector = function (elements) {
+ this._magnitude = 0
+ this.elements = elements || []
+}
+
+
+/**
+ * Calculates the position within the vector to insert a given index.
+ *
+ * This is used internally by insert and upsert. If there are duplicate indexes then
+ * the position is returned as if the value for that index were to be updated, but it
+ * is the callers responsibility to check whether there is a duplicate at that index
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.positionForIndex = function (index) {
+ // For an empty vector the tuple can be inserted at the beginning
+ if (this.elements.length == 0) {
+ return 0
+ }
+
+ var start = 0,
+ end = this.elements.length / 2,
+ sliceLength = end - start,
+ pivotPoint = Math.floor(sliceLength / 2),
+ pivotIndex = this.elements[pivotPoint * 2]
+
+ while (sliceLength > 1) {
+ if (pivotIndex < index) {
+ start = pivotPoint
+ }
+
+ if (pivotIndex > index) {
+ end = pivotPoint
+ }
+
+ if (pivotIndex == index) {
+ break
+ }
+
+ sliceLength = end - start
+ pivotPoint = start + Math.floor(sliceLength / 2)
+ pivotIndex = this.elements[pivotPoint * 2]
+ }
+
+ if (pivotIndex == index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex > index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex < index) {
+ return (pivotPoint + 1) * 2
+ }
+}
+
+/**
+ * Inserts an element at an index within the vector.
+ *
+ * Does not allow duplicates, will throw an error if there is already an entry
+ * for this index.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ */
+lunr.Vector.prototype.insert = function (insertIdx, val) {
+ this.upsert(insertIdx, val, function () {
+ throw "duplicate index"
+ })
+}
+
+/**
+ * Inserts or updates an existing index within the vector.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ * @param {function} fn - A function that is called for updates, the existing value and the
+ * requested value are passed as arguments
+ */
+lunr.Vector.prototype.upsert = function (insertIdx, val, fn) {
+ this._magnitude = 0
+ var position = this.positionForIndex(insertIdx)
+
+ if (this.elements[position] == insertIdx) {
+ this.elements[position + 1] = fn(this.elements[position + 1], val)
+ } else {
+ this.elements.splice(position, 0, insertIdx, val)
+ }
+}
+
+/**
+ * Calculates the magnitude of this vector.
+ *
+ * @returns {Number}
+ */
+lunr.Vector.prototype.magnitude = function () {
+ if (this._magnitude) return this._magnitude
+
+ var sumOfSquares = 0,
+ elementsLength = this.elements.length
+
+ for (var i = 1; i < elementsLength; i += 2) {
+ var val = this.elements[i]
+ sumOfSquares += val * val
+ }
+
+ return this._magnitude = Math.sqrt(sumOfSquares)
+}
+
+/**
+ * Calculates the dot product of this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The vector to compute the dot product with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.dot = function (otherVector) {
+ var dotProduct = 0,
+ a = this.elements, b = otherVector.elements,
+ aLen = a.length, bLen = b.length,
+ aVal = 0, bVal = 0,
+ i = 0, j = 0
+
+ while (i < aLen && j < bLen) {
+ aVal = a[i], bVal = b[j]
+ if (aVal < bVal) {
+ i += 2
+ } else if (aVal > bVal) {
+ j += 2
+ } else if (aVal == bVal) {
+ dotProduct += a[i + 1] * b[j + 1]
+ i += 2
+ j += 2
+ }
+ }
+
+ return dotProduct
+}
+
+/**
+ * Calculates the similarity between this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The other vector to calculate the
+ * similarity with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.similarity = function (otherVector) {
+ return this.dot(otherVector) / this.magnitude() || 0
+}
+
+/**
+ * Converts the vector to an array of the elements within the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toArray = function () {
+ var output = new Array (this.elements.length / 2)
+
+ for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {
+ output[j] = this.elements[i]
+ }
+
+ return output
+}
+
+/**
+ * A JSON serializable representation of the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toJSON = function () {
+ return this.elements
+}
+/* eslint-disable */
+/*!
+ * lunr.stemmer
+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */
+
+/**
+ * lunr.stemmer is an english language stemmer, this is a JavaScript
+ * implementation of the PorterStemmer taken from http://tartarus.org/~martin
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token - The string to stem
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ * @function
+ */
+lunr.stemmer = (function(){
+ var step2list = {
+ "ational" : "ate",
+ "tional" : "tion",
+ "enci" : "ence",
+ "anci" : "ance",
+ "izer" : "ize",
+ "bli" : "ble",
+ "alli" : "al",
+ "entli" : "ent",
+ "eli" : "e",
+ "ousli" : "ous",
+ "ization" : "ize",
+ "ation" : "ate",
+ "ator" : "ate",
+ "alism" : "al",
+ "iveness" : "ive",
+ "fulness" : "ful",
+ "ousness" : "ous",
+ "aliti" : "al",
+ "iviti" : "ive",
+ "biliti" : "ble",
+ "logi" : "log"
+ },
+
+ step3list = {
+ "icate" : "ic",
+ "ative" : "",
+ "alize" : "al",
+ "iciti" : "ic",
+ "ical" : "ic",
+ "ful" : "",
+ "ness" : ""
+ },
+
+ c = "[^aeiou]", // consonant
+ v = "[aeiouy]", // vowel
+ C = c + "[^aeiouy]*", // consonant sequence
+ V = v + "[aeiou]*", // vowel sequence
+
+ mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0
+ meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1
+ mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1
+ s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ var re_mgr0 = new RegExp(mgr0);
+ var re_mgr1 = new RegExp(mgr1);
+ var re_meq1 = new RegExp(meq1);
+ var re_s_v = new RegExp(s_v);
+
+ var re_1a = /^(.+?)(ss|i)es$/;
+ var re2_1a = /^(.+?)([^s])s$/;
+ var re_1b = /^(.+?)eed$/;
+ var re2_1b = /^(.+?)(ed|ing)$/;
+ var re_1b_2 = /.$/;
+ var re2_1b_2 = /(at|bl|iz)$/;
+ var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$");
+ var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var re_1c = /^(.+?[^aeiou])y$/;
+ var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+
+ var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+
+ var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ var re2_4 = /^(.+?)(s|t)(ion)$/;
+
+ var re_5 = /^(.+?)e$/;
+ var re_5_1 = /ll$/;
+ var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var porterStemmer = function porterStemmer(w) {
+ var stem,
+ suffix,
+ firstch,
+ re,
+ re2,
+ re3,
+ re4;
+
+ if (w.length < 3) { return w; }
+
+ firstch = w.substr(0,1);
+ if (firstch == "y") {
+ w = firstch.toUpperCase() + w.substr(1);
+ }
+
+ // Step 1a
+ re = re_1a
+ re2 = re2_1a;
+
+ if (re.test(w)) { w = w.replace(re,"$1$2"); }
+ else if (re2.test(w)) { w = w.replace(re2,"$1$2"); }
+
+ // Step 1b
+ re = re_1b;
+ re2 = re2_1b;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = re_mgr0;
+ if (re.test(fp[1])) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = re_s_v;
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = re2_1b_2;
+ re3 = re3_1b_2;
+ re4 = re4_1b_2;
+ if (re2.test(w)) { w = w + "e"; }
+ else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); }
+ else if (re4.test(w)) { w = w + "e"; }
+ }
+ }
+
+ // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)
+ re = re_1c;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = re_2;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step2list[suffix];
+ }
+ }
+
+ // Step 3
+ re = re_3;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step3list[suffix];
+ }
+ }
+
+ // Step 4
+ re = re_4;
+ re2 = re2_4;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ if (re.test(stem)) {
+ w = stem;
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = re_mgr1;
+ if (re2.test(stem)) {
+ w = stem;
+ }
+ }
+
+ // Step 5
+ re = re_5;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ re2 = re_meq1;
+ re3 = re3_5;
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
+ w = stem;
+ }
+ }
+
+ re = re_5_1;
+ re2 = re_mgr1;
+ if (re.test(w) && re2.test(w)) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+
+ if (firstch == "y") {
+ w = firstch.toLowerCase() + w.substr(1);
+ }
+
+ return w;
+ };
+
+ return function (token) {
+ return token.update(porterStemmer);
+ }
+})();
+
+lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')
+/*!
+ * lunr.stopWordFilter
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.generateStopWordFilter builds a stopWordFilter function from the provided
+ * list of stop words.
+ *
+ * The built in lunr.stopWordFilter is built using this generator and can be used
+ * to generate custom stopWordFilters for applications or non English languages.
+ *
+ * @function
+ * @param {Array} token The token to pass through the filter
+ * @returns {lunr.PipelineFunction}
+ * @see lunr.Pipeline
+ * @see lunr.stopWordFilter
+ */
+lunr.generateStopWordFilter = function (stopWords) {
+ var words = stopWords.reduce(function (memo, stopWord) {
+ memo[stopWord] = stopWord
+ return memo
+ }, {})
+
+ return function (token) {
+ if (token && words[token.toString()] !== token.toString()) return token
+ }
+}
+
+/**
+ * lunr.stopWordFilter is an English language stop word list filter, any words
+ * contained in the list will not be passed through the filter.
+ *
+ * This is intended to be used in the Pipeline. If the token does not pass the
+ * filter then undefined will be returned.
+ *
+ * @function
+ * @implements {lunr.PipelineFunction}
+ * @params {lunr.Token} token - A token to check for being a stop word.
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.stopWordFilter = lunr.generateStopWordFilter([
+ 'a',
+ 'able',
+ 'about',
+ 'across',
+ 'after',
+ 'all',
+ 'almost',
+ 'also',
+ 'am',
+ 'among',
+ 'an',
+ 'and',
+ 'any',
+ 'are',
+ 'as',
+ 'at',
+ 'be',
+ 'because',
+ 'been',
+ 'but',
+ 'by',
+ 'can',
+ 'cannot',
+ 'could',
+ 'dear',
+ 'did',
+ 'do',
+ 'does',
+ 'either',
+ 'else',
+ 'ever',
+ 'every',
+ 'for',
+ 'from',
+ 'get',
+ 'got',
+ 'had',
+ 'has',
+ 'have',
+ 'he',
+ 'her',
+ 'hers',
+ 'him',
+ 'his',
+ 'how',
+ 'however',
+ 'i',
+ 'if',
+ 'in',
+ 'into',
+ 'is',
+ 'it',
+ 'its',
+ 'just',
+ 'least',
+ 'let',
+ 'like',
+ 'likely',
+ 'may',
+ 'me',
+ 'might',
+ 'most',
+ 'must',
+ 'my',
+ 'neither',
+ 'no',
+ 'nor',
+ 'not',
+ 'of',
+ 'off',
+ 'often',
+ 'on',
+ 'only',
+ 'or',
+ 'other',
+ 'our',
+ 'own',
+ 'rather',
+ 'said',
+ 'say',
+ 'says',
+ 'she',
+ 'should',
+ 'since',
+ 'so',
+ 'some',
+ 'than',
+ 'that',
+ 'the',
+ 'their',
+ 'them',
+ 'then',
+ 'there',
+ 'these',
+ 'they',
+ 'this',
+ 'tis',
+ 'to',
+ 'too',
+ 'twas',
+ 'us',
+ 'wants',
+ 'was',
+ 'we',
+ 'were',
+ 'what',
+ 'when',
+ 'where',
+ 'which',
+ 'while',
+ 'who',
+ 'whom',
+ 'why',
+ 'will',
+ 'with',
+ 'would',
+ 'yet',
+ 'you',
+ 'your'
+])
+
+lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')
+/*!
+ * lunr.trimmer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.trimmer is a pipeline function for trimming non word
+ * characters from the beginning and end of tokens before they
+ * enter the index.
+ *
+ * This implementation may not work correctly for non latin
+ * characters and should either be removed or adapted for use
+ * with languages with non-latin characters.
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token The token to pass through the filter
+ * @returns {lunr.Token}
+ * @see lunr.Pipeline
+ */
+lunr.trimmer = function (token) {
+ return token.update(function (s) {
+ return s.replace(/^\W+/, '').replace(/\W+$/, '')
+ })
+}
+
+lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')
+/*!
+ * lunr.TokenSet
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A token set is used to store the unique list of all tokens
+ * within an index. Token sets are also used to represent an
+ * incoming query to the index, this query token set and index
+ * token set are then intersected to find which tokens to look
+ * up in the inverted index.
+ *
+ * A token set can hold multiple tokens, as in the case of the
+ * index token set, or it can hold a single token as in the
+ * case of a simple query token set.
+ *
+ * Additionally token sets are used to perform wildcard matching.
+ * Leading, contained and trailing wildcards are supported, and
+ * from this edit distance matching can also be provided.
+ *
+ * Token sets are implemented as a minimal finite state automata,
+ * where both common prefixes and suffixes are shared between tokens.
+ * This helps to reduce the space used for storing the token set.
+ *
+ * @constructor
+ */
+lunr.TokenSet = function () {
+ this.final = false
+ this.edges = {}
+ this.id = lunr.TokenSet._nextId
+ lunr.TokenSet._nextId += 1
+}
+
+/**
+ * Keeps track of the next, auto increment, identifier to assign
+ * to a new tokenSet.
+ *
+ * TokenSets require a unique identifier to be correctly minimised.
+ *
+ * @private
+ */
+lunr.TokenSet._nextId = 1
+
+/**
+ * Creates a TokenSet instance from the given sorted array of words.
+ *
+ * @param {String[]} arr - A sorted array of strings to create the set from.
+ * @returns {lunr.TokenSet}
+ * @throws Will throw an error if the input array is not sorted.
+ */
+lunr.TokenSet.fromArray = function (arr) {
+ var builder = new lunr.TokenSet.Builder
+
+ for (var i = 0, len = arr.length; i < len; i++) {
+ builder.insert(arr[i])
+ }
+
+ builder.finish()
+ return builder.root
+}
+
+/**
+ * Creates a token set from a query clause.
+ *
+ * @private
+ * @param {Object} clause - A single clause from lunr.Query.
+ * @param {string} clause.term - The query clause term.
+ * @param {number} [clause.editDistance] - The optional edit distance for the term.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromClause = function (clause) {
+ if ('editDistance' in clause) {
+ return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)
+ } else {
+ return lunr.TokenSet.fromString(clause.term)
+ }
+}
+
+/**
+ * Creates a token set representing a single string with a specified
+ * edit distance.
+ *
+ * Insertions, deletions, substitutions and transpositions are each
+ * treated as an edit distance of 1.
+ *
+ * Increasing the allowed edit distance will have a dramatic impact
+ * on the performance of both creating and intersecting these TokenSets.
+ * It is advised to keep the edit distance less than 3.
+ *
+ * @param {string} str - The string to create the token set from.
+ * @param {number} editDistance - The allowed edit distance to match.
+ * @returns {lunr.Vector}
+ */
+lunr.TokenSet.fromFuzzyString = function (str, editDistance) {
+ var root = new lunr.TokenSet
+
+ var stack = [{
+ node: root,
+ editsRemaining: editDistance,
+ str: str
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop()
+
+ // no edit
+ if (frame.str.length > 0) {
+ var char = frame.str.charAt(0),
+ noEditNode
+
+ if (char in frame.node.edges) {
+ noEditNode = frame.node.edges[char]
+ } else {
+ noEditNode = new lunr.TokenSet
+ frame.node.edges[char] = noEditNode
+ }
+
+ if (frame.str.length == 1) {
+ noEditNode.final = true
+ }
+
+ stack.push({
+ node: noEditNode,
+ editsRemaining: frame.editsRemaining,
+ str: frame.str.slice(1)
+ })
+ }
+
+ if (frame.editsRemaining == 0) {
+ continue
+ }
+
+ // insertion
+ if ("*" in frame.node.edges) {
+ var insertionNode = frame.node.edges["*"]
+ } else {
+ var insertionNode = new lunr.TokenSet
+ frame.node.edges["*"] = insertionNode
+ }
+
+ if (frame.str.length == 0) {
+ insertionNode.final = true
+ }
+
+ stack.push({
+ node: insertionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str
+ })
+
+ // deletion
+ // can only do a deletion if we have enough edits remaining
+ // and if there are characters left to delete in the string
+ if (frame.str.length > 1) {
+ stack.push({
+ node: frame.node,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // deletion
+ // just removing the last character from the str
+ if (frame.str.length == 1) {
+ frame.node.final = true
+ }
+
+ // substitution
+ // can only do a substitution if we have enough edits remaining
+ // and if there are characters left to substitute
+ if (frame.str.length >= 1) {
+ if ("*" in frame.node.edges) {
+ var substitutionNode = frame.node.edges["*"]
+ } else {
+ var substitutionNode = new lunr.TokenSet
+ frame.node.edges["*"] = substitutionNode
+ }
+
+ if (frame.str.length == 1) {
+ substitutionNode.final = true
+ }
+
+ stack.push({
+ node: substitutionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // transposition
+ // can only do a transposition if there are edits remaining
+ // and there are enough characters to transpose
+ if (frame.str.length > 1) {
+ var charA = frame.str.charAt(0),
+ charB = frame.str.charAt(1),
+ transposeNode
+
+ if (charB in frame.node.edges) {
+ transposeNode = frame.node.edges[charB]
+ } else {
+ transposeNode = new lunr.TokenSet
+ frame.node.edges[charB] = transposeNode
+ }
+
+ if (frame.str.length == 1) {
+ transposeNode.final = true
+ }
+
+ stack.push({
+ node: transposeNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: charA + frame.str.slice(2)
+ })
+ }
+ }
+
+ return root
+}
+
+/**
+ * Creates a TokenSet from a string.
+ *
+ * The string may contain one or more wildcard characters (*)
+ * that will allow wildcard matching when intersecting with
+ * another TokenSet.
+ *
+ * @param {string} str - The string to create a TokenSet from.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromString = function (str) {
+ var node = new lunr.TokenSet,
+ root = node
+
+ /*
+ * Iterates through all characters within the passed string
+ * appending a node for each character.
+ *
+ * When a wildcard character is found then a self
+ * referencing edge is introduced to continually match
+ * any number of any characters.
+ */
+ for (var i = 0, len = str.length; i < len; i++) {
+ var char = str[i],
+ final = (i == len - 1)
+
+ if (char == "*") {
+ node.edges[char] = node
+ node.final = final
+
+ } else {
+ var next = new lunr.TokenSet
+ next.final = final
+
+ node.edges[char] = next
+ node = next
+ }
+ }
+
+ return root
+}
+
+/**
+ * Converts this TokenSet into an array of strings
+ * contained within the TokenSet.
+ *
+ * This is not intended to be used on a TokenSet that
+ * contains wildcards, in these cases the results are
+ * undefined and are likely to cause an infinite loop.
+ *
+ * @returns {string[]}
+ */
+lunr.TokenSet.prototype.toArray = function () {
+ var words = []
+
+ var stack = [{
+ prefix: "",
+ node: this
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop(),
+ edges = Object.keys(frame.node.edges),
+ len = edges.length
+
+ if (frame.node.final) {
+ /* In Safari, at this point the prefix is sometimes corrupted, see:
+ * https://github.com/olivernn/lunr.js/issues/279 Calling any
+ * String.prototype method forces Safari to "cast" this string to what
+ * it's supposed to be, fixing the bug. */
+ frame.prefix.charAt(0)
+ words.push(frame.prefix)
+ }
+
+ for (var i = 0; i < len; i++) {
+ var edge = edges[i]
+
+ stack.push({
+ prefix: frame.prefix.concat(edge),
+ node: frame.node.edges[edge]
+ })
+ }
+ }
+
+ return words
+}
+
+/**
+ * Generates a string representation of a TokenSet.
+ *
+ * This is intended to allow TokenSets to be used as keys
+ * in objects, largely to aid the construction and minimisation
+ * of a TokenSet. As such it is not designed to be a human
+ * friendly representation of the TokenSet.
+ *
+ * @returns {string}
+ */
+lunr.TokenSet.prototype.toString = function () {
+ // NOTE: Using Object.keys here as this.edges is very likely
+ // to enter 'hash-mode' with many keys being added
+ //
+ // avoiding a for-in loop here as it leads to the function
+ // being de-optimised (at least in V8). From some simple
+ // benchmarks the performance is comparable, but allowing
+ // V8 to optimize may mean easy performance wins in the future.
+
+ if (this._str) {
+ return this._str
+ }
+
+ var str = this.final ? '1' : '0',
+ labels = Object.keys(this.edges).sort(),
+ len = labels.length
+
+ for (var i = 0; i < len; i++) {
+ var label = labels[i],
+ node = this.edges[label]
+
+ str = str + label + node.id
+ }
+
+ return str
+}
+
+/**
+ * Returns a new TokenSet that is the intersection of
+ * this TokenSet and the passed TokenSet.
+ *
+ * This intersection will take into account any wildcards
+ * contained within the TokenSet.
+ *
+ * @param {lunr.TokenSet} b - An other TokenSet to intersect with.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.prototype.intersect = function (b) {
+ var output = new lunr.TokenSet,
+ frame = undefined
+
+ var stack = [{
+ qNode: b,
+ output: output,
+ node: this
+ }]
+
+ while (stack.length) {
+ frame = stack.pop()
+
+ // NOTE: As with the #toString method, we are using
+ // Object.keys and a for loop instead of a for-in loop
+ // as both of these objects enter 'hash' mode, causing
+ // the function to be de-optimised in V8
+ var qEdges = Object.keys(frame.qNode.edges),
+ qLen = qEdges.length,
+ nEdges = Object.keys(frame.node.edges),
+ nLen = nEdges.length
+
+ for (var q = 0; q < qLen; q++) {
+ var qEdge = qEdges[q]
+
+ for (var n = 0; n < nLen; n++) {
+ var nEdge = nEdges[n]
+
+ if (nEdge == qEdge || qEdge == '*') {
+ var node = frame.node.edges[nEdge],
+ qNode = frame.qNode.edges[qEdge],
+ final = node.final && qNode.final,
+ next = undefined
+
+ if (nEdge in frame.output.edges) {
+ // an edge already exists for this character
+ // no need to create a new node, just set the finality
+ // bit unless this node is already final
+ next = frame.output.edges[nEdge]
+ next.final = next.final || final
+
+ } else {
+ // no edge exists yet, must create one
+ // set the finality bit and insert it
+ // into the output
+ next = new lunr.TokenSet
+ next.final = final
+ frame.output.edges[nEdge] = next
+ }
+
+ stack.push({
+ qNode: qNode,
+ output: next,
+ node: node
+ })
+ }
+ }
+ }
+ }
+
+ return output
+}
+lunr.TokenSet.Builder = function () {
+ this.previousWord = ""
+ this.root = new lunr.TokenSet
+ this.uncheckedNodes = []
+ this.minimizedNodes = {}
+}
+
+lunr.TokenSet.Builder.prototype.insert = function (word) {
+ var node,
+ commonPrefix = 0
+
+ if (word < this.previousWord) {
+ throw new Error ("Out of order word insertion")
+ }
+
+ for (var i = 0; i < word.length && i < this.previousWord.length; i++) {
+ if (word[i] != this.previousWord[i]) break
+ commonPrefix++
+ }
+
+ this.minimize(commonPrefix)
+
+ if (this.uncheckedNodes.length == 0) {
+ node = this.root
+ } else {
+ node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child
+ }
+
+ for (var i = commonPrefix; i < word.length; i++) {
+ var nextNode = new lunr.TokenSet,
+ char = word[i]
+
+ node.edges[char] = nextNode
+
+ this.uncheckedNodes.push({
+ parent: node,
+ char: char,
+ child: nextNode
+ })
+
+ node = nextNode
+ }
+
+ node.final = true
+ this.previousWord = word
+}
+
+lunr.TokenSet.Builder.prototype.finish = function () {
+ this.minimize(0)
+}
+
+lunr.TokenSet.Builder.prototype.minimize = function (downTo) {
+ for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {
+ var node = this.uncheckedNodes[i],
+ childKey = node.child.toString()
+
+ if (childKey in this.minimizedNodes) {
+ node.parent.edges[node.char] = this.minimizedNodes[childKey]
+ } else {
+ // Cache the key for this node since
+ // we know it can't change anymore
+ node.child._str = childKey
+
+ this.minimizedNodes[childKey] = node.child
+ }
+
+ this.uncheckedNodes.pop()
+ }
+}
+/*!
+ * lunr.Index
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * An index contains the built index of all documents and provides a query interface
+ * to the index.
+ *
+ * Usually instances of lunr.Index will not be created using this constructor, instead
+ * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
+ * used to load previously built and serialized indexes.
+ *
+ * @constructor
+ * @param {Object} attrs - The attributes of the built search index.
+ * @param {Object} attrs.invertedIndex - An index of term/field to document reference.
+ * @param {Object' + noResultsText + '
'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/0.0.1/search/search_index.json b/0.0.1/search/search_index.json new file mode 100644 index 0000000..31162a5 --- /dev/null +++ b/0.0.1/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to segpy's documentation! Segpy is a comprehensive pipeline designed for segregation analysis. This documentation provides a step-by-step tutorial on executing segregation analysis in a High-Performance Computing (HPC) environment utilizing the Slurm workload manager system (https://slurm.schedmd.com/), on a Linux workstation, or directly through the segpy module in Python. Segregation analysis is a crucial process for exploring genetic variants within a sample of sequence data. This pipeline facilitates the counting of affected and non-affected individuals with variants, including homozygous variants, those with no variants, and those with no calls. These counts are computed both within families and globally. Additionally, the pipeline offers a detailed breakdown not only of variants but also of alleles in each case. To execute segregation analysis successfully, it is imperative to have a pedigree file with six columns: familyid , individualid , parentalid , maternalid , sex {1:male; 2:female, 0:unknown}, and phenotype ={1: control (unaffected), 2: proband(affected), -9:missing}. The genetic data must be provided in the vcf format. For guidance on how to use segpy's pipeline, consult the tutorial. Contents Installation Tutorial: segpy module segpy slurm segpy local container FAQ Reference","title":"Home"},{"location":"#welcome-to-segpys-documentation","text":"Segpy is a comprehensive pipeline designed for segregation analysis. This documentation provides a step-by-step tutorial on executing segregation analysis in a High-Performance Computing (HPC) environment utilizing the Slurm workload manager system (https://slurm.schedmd.com/), on a Linux workstation, or directly through the segpy module in Python. Segregation analysis is a crucial process for exploring genetic variants within a sample of sequence data. This pipeline facilitates the counting of affected and non-affected individuals with variants, including homozygous variants, those with no variants, and those with no calls. These counts are computed both within families and globally. Additionally, the pipeline offers a detailed breakdown not only of variants but also of alleles in each case. To execute segregation analysis successfully, it is imperative to have a pedigree file with six columns: familyid , individualid , parentalid , maternalid , sex {1:male; 2:female, 0:unknown}, and phenotype ={1: control (unaffected), 2: proband(affected), -9:missing}. The genetic data must be provided in the vcf format. For guidance on how to use segpy's pipeline, consult the tutorial.","title":"Welcome to segpy's documentation!"},{"location":"#contents","text":"Installation Tutorial: segpy module segpy slurm segpy local container FAQ Reference","title":"Contents"},{"location":"Acknowledgement/","text":"Acknowledgement The pipeline is done as part of MNI projects, it is written by Saeid Amiri with associate with Dan Spiegelman, and Sali Farhan at Neuro Bioinformatics Core. Copyright belongs MNI BIOINFO CORE .","title":"- Acknowledgement"},{"location":"Acknowledgement/#acknowledgement","text":"The pipeline is done as part of MNI projects, it is written by Saeid Amiri with associate with Dan Spiegelman, and Sali Farhan at Neuro Bioinformatics Core. Copyright belongs MNI BIOINFO CORE .","title":"Acknowledgement"},{"location":"FAQ/","text":"Frequently asked questions What is finalseg.csv What is finalseg.csv The output of finalseg.csv can be categorized to 1) locus and alleles, 2) CSQ, 3) Global- Non-Affected 4) Global-Affected, 5) Family, 6) Family-Affected 7) Family - Non-affected. If you do not want to have CSQ in the output file, choose CSQ=False . locus and alleles locus: chromosome alleles: a variant form of a gene CSQ VEP put all the requested information in infront CSQ. Global - Non-Affected- is dropt glb_naf_wild: Global - Non-Affecteds, wildtype glb_naf_ncl: Global - Non-Affecteds, no call glb_naf_vrt: Global - Non-Affecteds, with variant glb_naf_homv: Global - Non-Affecteds, homozygous for ALT allele glb_naf_altaf: Global - Non-Affecteds, ALT allele frequency Global - Affected - is dropt glb_aff_wild: Global - Affecteds, wildtype glb_aff_ncl: Global - Affecteds, no call glb_aff_vrt: Global - Affecteds, with variant glb_aff_homv: Global - Affecteds, homozygous for ALT allele glb_aff_altaf: Global - Affecteds, ALT allele frequency Family- Is dropt {famid}_wild: Family - Affecteds: wildtype {famid}_ncl: Family - Affecteds: no call {famid}_vrt: Family - Affecteds: with variant {famid}_homv: Family - Affecteds: homozygous for ALT allele Family - Affected {famid}_aff_wild: Family - Affecteds: wildtype {famid}_aff_ncl: Family - Affecteds: no call {famid}_aff_vrt: Family - Affecteds: with variant {famid}_aff_homv: Family - Affecteds: homozygous for ALT allele Family - Nonaffected {famid}_wild_naf: Family - Nonaffecteds: wildtype {famid}_ncl_naf: Family - Nonaffecteds: no call {famid}_vrt_naf: Family - Nonaffecteds: with variant {famid}_homv_naf: Family - Nonaffecteds: homozygous for ALT allele","title":"FAQ"},{"location":"FAQ/#frequently-asked-questions","text":"What is finalseg.csv","title":"Frequently asked questions"},{"location":"FAQ/#what-is-finalsegcsv","text":"The output of finalseg.csv can be categorized to 1) locus and alleles, 2) CSQ, 3) Global- Non-Affected 4) Global-Affected, 5) Family, 6) Family-Affected 7) Family - Non-affected. If you do not want to have CSQ in the output file, choose CSQ=False .","title":"What is finalseg.csv"},{"location":"FAQ/#locus-and-alleles","text":"locus: chromosome alleles: a variant form of a gene","title":"locus and alleles"},{"location":"FAQ/#csq","text":"VEP put all the requested information in infront CSQ.","title":"CSQ"},{"location":"FAQ/#global-non-affected-is-dropt","text":"glb_naf_wild: Global - Non-Affecteds, wildtype glb_naf_ncl: Global - Non-Affecteds, no call glb_naf_vrt: Global - Non-Affecteds, with variant glb_naf_homv: Global - Non-Affecteds, homozygous for ALT allele glb_naf_altaf: Global - Non-Affecteds, ALT allele frequency","title":"Global - Non-Affected- is dropt"},{"location":"FAQ/#global-affected-is-dropt","text":"glb_aff_wild: Global - Affecteds, wildtype glb_aff_ncl: Global - Affecteds, no call glb_aff_vrt: Global - Affecteds, with variant glb_aff_homv: Global - Affecteds, homozygous for ALT allele glb_aff_altaf: Global - Affecteds, ALT allele frequency","title":"Global - Affected - is dropt"},{"location":"FAQ/#family-is-dropt","text":"{famid}_wild: Family - Affecteds: wildtype {famid}_ncl: Family - Affecteds: no call {famid}_vrt: Family - Affecteds: with variant {famid}_homv: Family - Affecteds: homozygous for ALT allele","title":"Family- Is dropt"},{"location":"FAQ/#family-affected","text":"{famid}_aff_wild: Family - Affecteds: wildtype {famid}_aff_ncl: Family - Affecteds: no call {famid}_aff_vrt: Family - Affecteds: with variant {famid}_aff_homv: Family - Affecteds: homozygous for ALT allele","title":"Family - Affected"},{"location":"FAQ/#family-nonaffected","text":"{famid}_wild_naf: Family - Nonaffecteds: wildtype {famid}_ncl_naf: Family - Nonaffecteds: no call {famid}_vrt_naf: Family - Nonaffecteds: with variant {famid}_homv_naf: Family - Nonaffecteds: homozygous for ALT allele","title":"Family - Nonaffected"},{"location":"LICENSE/","text":"MIT License Copyright (c) 2022 The Neuro Bioinformatics Core Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.","title":"- License"},{"location":"about/","text":"Coming soon","title":"Coming soon"},{"location":"about/#coming-soon","text":"","title":"Coming soon"},{"location":"changelog/","text":"Changelog v0.0.1 This is the initial release. v0.0.2 Supposed to Complete the documentation","title":"- Changelog"},{"location":"changelog/#changelog","text":"","title":"Changelog"},{"location":"changelog/#v001","text":"This is the initial release.","title":"v0.0.1"},{"location":"changelog/#v002","text":"Supposed to Complete the documentation","title":"v0.0.2"},{"location":"container/","text":"Container (Under review) The pipeline can be executed using a Singularity container, eliminating the need to install modules within the pipeline. It is compatible for use on High-Performance Computing (HPC) systems or any standard workstation linux system. Contents Step 1: Download Step 2: Run Download Download the segregation image. singularity pull library://saeidamiri1/mni/seg_cont.sif:latest Run Execute singularity exec ./seg_cont.sif launch_segpy.sh -h to ensure the pipeline is functioning correctly. ------------------------------------ Segregation pipline version 0.2.2.3 Usage: /usr/local/bin/launch_segpy.sh [arguments] mandatory arguments: -d (--dir) = Working directory (where all the outputs will be printed) (give full path) -s (--steps) = Specify what steps, e.g., 2 to run just step 2, 1-3 (run steps 1 through 3). 'ALL' to run all steps. steps: 0: initial setup 1: create hail matrix 2: run segregation 3: final cleanup and formatting optional arguments: -h (--help) = Get the program options and exit. --parser = 'general': to general parsing, 'unique': drop multiplicities -v (--vcf) = VCF file (mandatory for steps 1-3) -p (--ped) = PED file (mandatory for steps 1-3) -c (--config) = config file [CURRENT: \"/usr/local/bin/configs/segpy.config.ini\"] -V (--verbose) = verbose output You can import the data and working directory into the container using --bind , and the remaining steps are identical to those outlined in segpy local . singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 0 singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 1 --vcf ~/seg_cont/outfolder/data/VEP_iPSC.vcf singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 2 --ped ~/seg_cont/outfolder/data/iPSC_2.ped singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 3 --parser general \u2b06 back to top","title":"- - container"},{"location":"container/#container-under-review","text":"The pipeline can be executed using a Singularity container, eliminating the need to install modules within the pipeline. It is compatible for use on High-Performance Computing (HPC) systems or any standard workstation linux system.","title":"Container (Under review)"},{"location":"container/#contents","text":"Step 1: Download Step 2: Run","title":"Contents"},{"location":"container/#download","text":"Download the segregation image. singularity pull library://saeidamiri1/mni/seg_cont.sif:latest","title":"Download"},{"location":"container/#run","text":"Execute singularity exec ./seg_cont.sif launch_segpy.sh -h to ensure the pipeline is functioning correctly. ------------------------------------ Segregation pipline version 0.2.2.3 Usage: /usr/local/bin/launch_segpy.sh [arguments] mandatory arguments: -d (--dir) = Working directory (where all the outputs will be printed) (give full path) -s (--steps) = Specify what steps, e.g., 2 to run just step 2, 1-3 (run steps 1 through 3). 'ALL' to run all steps. steps: 0: initial setup 1: create hail matrix 2: run segregation 3: final cleanup and formatting optional arguments: -h (--help) = Get the program options and exit. --parser = 'general': to general parsing, 'unique': drop multiplicities -v (--vcf) = VCF file (mandatory for steps 1-3) -p (--ped) = PED file (mandatory for steps 1-3) -c (--config) = config file [CURRENT: \"/usr/local/bin/configs/segpy.config.ini\"] -V (--verbose) = verbose output You can import the data and working directory into the container using --bind , and the remaining steps are identical to those outlined in segpy local . singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 0 singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 1 --vcf ~/seg_cont/outfolder/data/VEP_iPSC.vcf singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 2 --ped ~/seg_cont/outfolder/data/iPSC_2.ped singularity exec --bind ~/seg_cont/outfolder ./segr_cont.img launch_segpy.sh -d ~/seg_cont/outfolder/run --steps 3 --parser general \u2b06 back to top","title":"Run"},{"location":"contributing/","text":"Contributing This is an initial version, and any contributions or suggestions are welcomed. The pipeline is designed to be user-friendly for segregation analysis. For direct contact, please reach out to The Neuro Bioinformatics Core via [neurobioinfo@mcgill.ca]. If you encounter any issue , please report them on the GitHub repository.","title":"- Contributing"},{"location":"contributing/#contributing","text":"This is an initial version, and any contributions or suggestions are welcomed. The pipeline is designed to be user-friendly for segregation analysis. For direct contact, please reach out to The Neuro Bioinformatics Core via [neurobioinfo@mcgill.ca]. If you encounter any issue , please report them on the GitHub repository.","title":"Contributing"},{"location":"installation/","text":"Installation I am HERE. segpy is a Python module specifically designed for segregation analysis, developed on Python 3.10.2. The module can be easily downloaded using pip package manager: pip install 'git+https://github.com/neurobioinfo/segpy#subdirectory=segpy' Segregation analysis can be conducted utilizing the segpy scheduler, segpy.pip . If you have access to an HPC or Linux workstation, you can automate the process using segpy.pip . The scheduler script is written in Bash, making it compatible with systems such as Slurm or a Linux workstation. wget https://github.com/neurobioinfo/segpy/releases/download/v0.2.2.3/segpy.pip.zip unzip segpy.pip.zip To obtain a brief overview of the pipeline, run the following code. bash ./segpy.pip/launch_segpy.sh -h","title":"Installation"},{"location":"installation/#installation","text":"I am HERE. segpy is a Python module specifically designed for segregation analysis, developed on Python 3.10.2. The module can be easily downloaded using pip package manager: pip install 'git+https://github.com/neurobioinfo/segpy#subdirectory=segpy' Segregation analysis can be conducted utilizing the segpy scheduler, segpy.pip . If you have access to an HPC or Linux workstation, you can automate the process using segpy.pip . The scheduler script is written in Bash, making it compatible with systems such as Slurm or a Linux workstation. wget https://github.com/neurobioinfo/segpy/releases/download/v0.2.2.3/segpy.pip.zip unzip segpy.pip.zip To obtain a brief overview of the pipeline, run the following code. bash ./segpy.pip/launch_segpy.sh -h","title":"Installation"},{"location":"reference/","text":"Adjustable execution parameters for the segregation pipeline Introduction Segpy parameters Introduction The configs/ directory contains the segpy.config.ini file which allows users to specify the parameters of interest explained below Segpy parameters ACCOUNT Specify user name for slurm module. SPARK_PATH Environment variables to point to the correct Spark executable ENV_PATH Environment variables to point to the correct Python executable PYTHON_CMD=python3.10 MODULEUSE The path to the environmental module JAVA_CMD=java JAVA_VERSION=11.0.2 Specify the version of Java NCOL=7 To control usage of memory to process CSQ. CSQ=True To process CSQ in the output, Consequence annotations from Ensembl VEP. GRCH=GRCh38 Genome Reference Consortium Human SPARKMEM=\"16g\" Spark Memory JAVATOOLOPTIONS=\"-Xmx8g\" Java Memory and any other options can be added to here. [step 1] Create table matrix WALLTIME_ARRAY[\"step_1\"]=00-01:00 Set a limit on the total run time of the job allocation in the step 1 under slurm. THREADS_ARRAY[\"step_1\"]=4 Request the maximum ntasks be invoked on each core in the step 1 under slurm. MEM_ARRAY[\"step_1\"]=25g Specify the real memory required per node in the step 1 under slurm. [step 2] Run segregation WALLTIME_ARRAY[\"step_2\"]=00-02:00 Set a limit on the total run time of the job allocation in the step 2 under slurm. THREADS_ARRAY[\"step_2\"]=4 Request the maximum ntasks be invoked on each core in the step 2 under slurm. MEM_ARRAY[\"step_2\"]=17g Specify the real memory required per node in the step 2 under slurm. INFO_REQUIRED=[1,2,3,4] One can determine the desired content to be included in the output 1: Global affected, 2: Global unaffected, 3: family-wise, 4: phenotype-family-wise, 5: phenotype-family-wise and non-include, 6: phenotype-family-wise-multipe sample, 7: phenotype-family-wise-multipe sample and non-include [step 3] Parsing WALLTIME_ARRAY[\"step_3\"]=00-01:00 Set a limit on the total run time of the job allocation in the step 3 under slurm. THREADS_ARRAY[\"step_3\"]=4 Request the maximum ntasks be invoked on each core in the step 3 under slurm. MEM_ARRAY[\"step_3\"]=4g Specify the real memory required per node in the step 3 under slurm.","title":"Reference"},{"location":"reference/#adjustable-execution-parameters-for-the-segregation-pipeline","text":"Introduction Segpy parameters","title":"Adjustable execution parameters for the segregation pipeline"},{"location":"reference/#introduction","text":"The configs/ directory contains the segpy.config.ini file which allows users to specify the parameters of interest explained below","title":"Introduction"},{"location":"reference/#segpy-parameters","text":"ACCOUNT Specify user name for slurm module. SPARK_PATH Environment variables to point to the correct Spark executable ENV_PATH Environment variables to point to the correct Python executable PYTHON_CMD=python3.10 MODULEUSE The path to the environmental module JAVA_CMD=java JAVA_VERSION=11.0.2 Specify the version of Java NCOL=7 To control usage of memory to process CSQ. CSQ=True To process CSQ in the output, Consequence annotations from Ensembl VEP. GRCH=GRCh38 Genome Reference Consortium Human SPARKMEM=\"16g\" Spark Memory JAVATOOLOPTIONS=\"-Xmx8g\" Java Memory and any other options can be added to here.","title":"Segpy parameters"},{"location":"reference/#step-1-create-table-matrix","text":"WALLTIME_ARRAY[\"step_1\"]=00-01:00 Set a limit on the total run time of the job allocation in the step 1 under slurm. THREADS_ARRAY[\"step_1\"]=4 Request the maximum ntasks be invoked on each core in the step 1 under slurm. MEM_ARRAY[\"step_1\"]=25g Specify the real memory required per node in the step 1 under slurm.","title":"[step 1] Create table matrix"},{"location":"reference/#step-2-run-segregation","text":"WALLTIME_ARRAY[\"step_2\"]=00-02:00 Set a limit on the total run time of the job allocation in the step 2 under slurm. THREADS_ARRAY[\"step_2\"]=4 Request the maximum ntasks be invoked on each core in the step 2 under slurm. MEM_ARRAY[\"step_2\"]=17g Specify the real memory required per node in the step 2 under slurm. INFO_REQUIRED=[1,2,3,4] One can determine the desired content to be included in the output 1: Global affected, 2: Global unaffected, 3: family-wise, 4: phenotype-family-wise, 5: phenotype-family-wise and non-include, 6: phenotype-family-wise-multipe sample, 7: phenotype-family-wise-multipe sample and non-include","title":"[step 2] Run segregation"},{"location":"reference/#step-3-parsing","text":"WALLTIME_ARRAY[\"step_3\"]=00-01:00 Set a limit on the total run time of the job allocation in the step 3 under slurm. THREADS_ARRAY[\"step_3\"]=4 Request the maximum ntasks be invoked on each core in the step 3 under slurm. MEM_ARRAY[\"step_3\"]=4g Specify the real memory required per node in the step 3 under slurm.","title":"[step 3] Parsing"},{"location":"segpy_local/","text":"Segpy on workstation segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on Linux workstation. Contents Step 0: Setup Step 1: Create table matrix Step 2: Run segregation Step 3: Clean final data The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system. segpy.svn workflow To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file. export PIPELINE_HOME=~/segpy.slurm PWD=~/outfolder VCF=~/data/VEP_iPSC.vcf PED=~/data/iPSC_2.ped Step 0: Setup Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 0 \\ --mode local Step 1: Create table matrix The following code, create MatrixTable from the VCF file. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1 \\ --vcf ${VCF} Step 2: Run segregation Execute the following code to generate the segregation. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 2 \\ --vcf ${VCF} \\ --ped ${PED} Step 3: Parsing To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser general The following code eliminates duplicate information in CSQ. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser unique Note You can execute steps 1 to 3 sequentially, as illustrated below. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1-3 \\ --vcf ${VCF} \\ --ped ${PED} \\ --parser general \u2b06 back to top","title":"- - segpy local"},{"location":"segpy_local/#segpy-on-workstation","text":"segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on Linux workstation.","title":"Segpy on workstation"},{"location":"segpy_local/#contents","text":"Step 0: Setup Step 1: Create table matrix Step 2: Run segregation Step 3: Clean final data The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system. segpy.svn workflow To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file. export PIPELINE_HOME=~/segpy.slurm PWD=~/outfolder VCF=~/data/VEP_iPSC.vcf PED=~/data/iPSC_2.ped","title":"Contents"},{"location":"segpy_local/#step-0-setup","text":"Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 0 \\ --mode local","title":"Step 0: Setup"},{"location":"segpy_local/#step-1-create-table-matrix","text":"The following code, create MatrixTable from the VCF file. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1 \\ --vcf ${VCF}","title":"Step 1: Create table matrix"},{"location":"segpy_local/#step-2-run-segregation","text":"Execute the following code to generate the segregation. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 2 \\ --vcf ${VCF} \\ --ped ${PED}","title":"Step 2: Run segregation"},{"location":"segpy_local/#step-3-parsing","text":"To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser general The following code eliminates duplicate information in CSQ. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser unique","title":"Step 3: Parsing"},{"location":"segpy_local/#note","text":"You can execute steps 1 to 3 sequentially, as illustrated below. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1-3 \\ --vcf ${VCF} \\ --ped ${PED} \\ --parser general \u2b06 back to top","title":"Note"},{"location":"segpy_module/","text":"Segpy module The following steps illustrate how to execute the segregation pipeline directly by utilizing the Segpy module within the Python editor. segpy workflow Contents Step 1: Run Spark Step 2: Create table matrix Step 3: Run segregation Step 4: Parsing Step 5: Shut down spark Step 1: Run Spark Initially, activate Spark on your system. export SPARK_HOME=$HOME/spark-3.1.3-bin-hadoop3.2 export SPARK_LOG_DIR=$HOME/temp module load java/11.0.2 ${SPARK_HOME}/sbin/start-master.sh Step 2: Create table matrix Following that, initialize Hail, import your VCF file, and save it as a matrix table. The matrix table serves as a data structure for representing genetic data in matrix form. Below, we import the VCF file, save it as a MatrixTable, and proceed to read your matrix table. In the tutorial, additional data is incorporated to test the pipeline, which can be found at [https://github.com/The-Neuro-Bioinformatics-Core/segpy/data]. This dataset is utilized to illustrate the pipeline's functionality. The subsequent code imports a VCF file and represents it as a MatrixTable: import sys import pandas as pd import hail as hl hl.import_vcf('~/test/data/VEP.iPSC.vcf',force=True,reference_genome='GRCh38',array_elements_required=False).write('~/test/output/VEP.iPSC.mt', overwrite=True) mt = hl.read_matrix_table('~/test/output/VEP.iPSC.mt') Step 3: Run segregation Execute the following code to generate the segregation. from segpy import seg ped=pd.read_csv('~/test/data/iPSC_2.ped'.ped',sep='\\t') destfolder= '~/test/output/' vcffile='~/test/data/VEP.iPSC.vcf' ncol=7 CSQ=True seg.run(mt,ped,outfolder,hl,ncol,CSQ,vcffile) Running the provided code generates two files, namely header.txt and finalseg.csv , in the destfolder . The header.txt file contains the header information found in finalseg.csv . The output in finalseg.csv is organized into the following categories: 1) locus and alleles, 2) CSQ, 3) Global-Non-Affected, 4) Global-Affected, 5) Family, 6) Family-Affected, and 7) Family-Non-Affected. If you prefer not to include CSQ in the output file, you can select CSQ=False . Step 4: Parsing To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. from segpy import parser parser.clean_general(outfolder) The information consists of various components; to eliminate redundancy, execute the following code. parser.clean_unique(outfolder) Step 5: Shut down spark Do not forget to deactivate environment and stop the spark: ${SPARK_HOME}/sbin/stop-master.sh \u2b06 back to top","title":"- - segpy module"},{"location":"segpy_module/#segpy-module","text":"The following steps illustrate how to execute the segregation pipeline directly by utilizing the Segpy module within the Python editor. segpy workflow","title":"Segpy module"},{"location":"segpy_module/#contents","text":"Step 1: Run Spark Step 2: Create table matrix Step 3: Run segregation Step 4: Parsing Step 5: Shut down spark","title":"Contents"},{"location":"segpy_module/#step-1-run-spark","text":"Initially, activate Spark on your system. export SPARK_HOME=$HOME/spark-3.1.3-bin-hadoop3.2 export SPARK_LOG_DIR=$HOME/temp module load java/11.0.2 ${SPARK_HOME}/sbin/start-master.sh","title":"Step 1: Run Spark"},{"location":"segpy_module/#step-2-create-table-matrix","text":"Following that, initialize Hail, import your VCF file, and save it as a matrix table. The matrix table serves as a data structure for representing genetic data in matrix form. Below, we import the VCF file, save it as a MatrixTable, and proceed to read your matrix table. In the tutorial, additional data is incorporated to test the pipeline, which can be found at [https://github.com/The-Neuro-Bioinformatics-Core/segpy/data]. This dataset is utilized to illustrate the pipeline's functionality. The subsequent code imports a VCF file and represents it as a MatrixTable: import sys import pandas as pd import hail as hl hl.import_vcf('~/test/data/VEP.iPSC.vcf',force=True,reference_genome='GRCh38',array_elements_required=False).write('~/test/output/VEP.iPSC.mt', overwrite=True) mt = hl.read_matrix_table('~/test/output/VEP.iPSC.mt')","title":"Step 2: Create table matrix"},{"location":"segpy_module/#step-3-run-segregation","text":"Execute the following code to generate the segregation. from segpy import seg ped=pd.read_csv('~/test/data/iPSC_2.ped'.ped',sep='\\t') destfolder= '~/test/output/' vcffile='~/test/data/VEP.iPSC.vcf' ncol=7 CSQ=True seg.run(mt,ped,outfolder,hl,ncol,CSQ,vcffile) Running the provided code generates two files, namely header.txt and finalseg.csv , in the destfolder . The header.txt file contains the header information found in finalseg.csv . The output in finalseg.csv is organized into the following categories: 1) locus and alleles, 2) CSQ, 3) Global-Non-Affected, 4) Global-Affected, 5) Family, 6) Family-Affected, and 7) Family-Non-Affected. If you prefer not to include CSQ in the output file, you can select CSQ=False .","title":"Step 3: Run segregation"},{"location":"segpy_module/#step-4-parsing","text":"To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. from segpy import parser parser.clean_general(outfolder) The information consists of various components; to eliminate redundancy, execute the following code. parser.clean_unique(outfolder)","title":"Step 4: Parsing"},{"location":"segpy_module/#step-5-shut-down-spark","text":"Do not forget to deactivate environment and stop the spark: ${SPARK_HOME}/sbin/stop-master.sh \u2b06 back to top","title":"Step 5: Shut down spark"},{"location":"segpy_slurm/","text":"Segpy via slurm segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on an HPC system utilizing the Slurm scheduler. This pipeline has been employed on Beluga , an HPC system utilizing the Slurm system. Contents Step 0: Setup Step 1: Create table matrix Step 2: Run segregation Step 3: Clean final data The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system. segpy.svn workflow To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file. export PIPELINE_HOME=~/segpy.slurm PWD=~/outfolder VCF=~/data/VEP_iPSC.vcf PED=~/data/iPSC_2.ped Step 0: Setup Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 0 \\ --mode slurm Step 1: Create table matrix The following code, create MatrixTable from the VCF file. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1 \\ --vcf ${VCF} Step 2: Run segregation Execute the following code to generate the segregation. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 2 \\ --vcf ${VCF} \\ --ped ${PED} Step 3: Parsing To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser general The following code eliminates duplicate information in CSQ. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser unique Note You can execute steps 1 to 3 sequentially, as illustrated below. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1-3 \\ --vcf ${VCF} \\ --ped ${PED} \\ --parser general \u2b06 back to top","title":"- - segpy slurm"},{"location":"segpy_slurm/#segpy-via-slurm","text":"segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on an HPC system utilizing the Slurm scheduler. This pipeline has been employed on Beluga , an HPC system utilizing the Slurm system.","title":"Segpy via slurm"},{"location":"segpy_slurm/#contents","text":"Step 0: Setup Step 1: Create table matrix Step 2: Run segregation Step 3: Clean final data The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system. segpy.svn workflow To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file. export PIPELINE_HOME=~/segpy.slurm PWD=~/outfolder VCF=~/data/VEP_iPSC.vcf PED=~/data/iPSC_2.ped","title":"Contents"},{"location":"segpy_slurm/#step-0-setup","text":"Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 0 \\ --mode slurm","title":"Step 0: Setup"},{"location":"segpy_slurm/#step-1-create-table-matrix","text":"The following code, create MatrixTable from the VCF file. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1 \\ --vcf ${VCF}","title":"Step 1: Create table matrix"},{"location":"segpy_slurm/#step-2-run-segregation","text":"Execute the following code to generate the segregation. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 2 \\ --vcf ${VCF} \\ --ped ${PED}","title":"Step 2: Run segregation"},{"location":"segpy_slurm/#step-3-parsing","text":"To parse the file and remove unnecessary characters such as \", [, ], etc., run the following code. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser general The following code eliminates duplicate information in CSQ. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 3 \\ --parser unique","title":"Step 3: Parsing"},{"location":"segpy_slurm/#note","text":"You can execute steps 1 to 3 sequentially, as illustrated below. sh $PIPELINE_HOME/launch_pipeline.segpy.sh \\ -d ${PWD} \\ --steps 1-3 \\ --vcf ${VCF} \\ --ped ${PED} \\ --parser general \u2b06 back to top","title":"Note"},{"location":"tutorial/","text":"Tutorial This section contains tutorials for segregation analysis. segpy module segpy slurm segpy local container","title":"Tutorial"},{"location":"tutorial/#tutorial","text":"This section contains tutorials for segregation analysis.","title":"Tutorial"},{"location":"tutorial/#segpy-module","text":"","title":"segpy module"},{"location":"tutorial/#segpy-slurm","text":"","title":"segpy slurm"},{"location":"tutorial/#segpy-local","text":"","title":"segpy local"},{"location":"tutorial/#container","text":"","title":"container"}]} \ No newline at end of file diff --git a/0.0.1/search/worker.js b/0.0.1/search/worker.js new file mode 100644 index 0000000..8628dbc --- /dev/null +++ b/0.0.1/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/0.0.1/segpy_local/index.html b/0.0.1/segpy_local/index.html new file mode 100644 index 0000000..93169d6 --- /dev/null +++ b/0.0.1/segpy_local/index.html @@ -0,0 +1,242 @@ + + + + + + + +
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+ Segpy on workstation
+segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on Linux workstation.
Contents
+ +The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system.
+ |
+
|---|
| segpy.svn workflow | +
To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file.
+export PIPELINE_HOME=~/segpy.slurm
+PWD=~/outfolder
+VCF=~/data/VEP_iPSC.vcf
+PED=~/data/iPSC_2.ped
+Step 0: Setup
+Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 0 \
+--mode local
+Step 1: Create table matrix
+The following code, create MatrixTable from the VCF file.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 1 \
+--vcf ${VCF}
+Step 2: Run segregation
+Execute the following code to generate the segregation.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 2 \
+--vcf ${VCF} \
+--ped ${PED}
+Step 3: Parsing
+To parse the file and remove unnecessary characters such as ", [, ], etc., run the following code.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 3 \
+--parser general
+The following code eliminates duplicate information in CSQ.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 3 \
+--parser unique
+Note
+You can execute steps 1 to 3 sequentially, as illustrated below.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 1-3 \
+--vcf ${VCF} \
+--ped ${PED} \
+--parser general
+
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+ « Previous
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diff --git a/0.0.1/segpy_module/index.html b/0.0.1/segpy_module/index.html
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+ Segpy module
+The following steps illustrate how to execute the segregation pipeline directly by utilizing the Segpy module within the Python editor.
+ |
+
|---|
| segpy workflow | +
Contents
+-
+
- Step 1: Run Spark +
- Step 2: Create table matrix +
- Step 3: Run segregation +
- Step 4: Parsing +
- Step 5: Shut down spark +
Step 1: Run Spark
+Initially, activate Spark on your system.
+export SPARK_HOME=$HOME/spark-3.1.3-bin-hadoop3.2
+export SPARK_LOG_DIR=$HOME/temp
+module load java/11.0.2
+${SPARK_HOME}/sbin/start-master.sh
+Step 2: Create table matrix
+Following that, initialize Hail, import your VCF file, and save it as a matrix table. The matrix table serves as a data structure for representing genetic data in matrix form. Below, we import the VCF file, save it as a MatrixTable, and proceed to read your matrix table. In the tutorial, additional data is incorporated to test the pipeline, which can be found at [https://github.com/The-Neuro-Bioinformatics-Core/segpy/data]. This dataset is utilized to illustrate the pipeline's functionality.
+The subsequent code imports a VCF file and represents it as a MatrixTable:
+import sys
+import pandas as pd
+import hail as hl
+hl.import_vcf('~/test/data/VEP.iPSC.vcf',force=True,reference_genome='GRCh38',array_elements_required=False).write('~/test/output/VEP.iPSC.mt', overwrite=True)
+mt = hl.read_matrix_table('~/test/output/VEP.iPSC.mt')
+Step 3: Run segregation
+Execute the following code to generate the segregation.
+from segpy import seg
+ped=pd.read_csv('~/test/data/iPSC_2.ped'.ped',sep='\t')
+destfolder= '~/test/output/'
+vcffile='~/test/data/VEP.iPSC.vcf'
+ncol=7
+CSQ=True
+seg.run(mt,ped,outfolder,hl,ncol,CSQ,vcffile)
+Running the provided code generates two files, namely header.txt and finalseg.csv, in the destfolder. The header.txt file contains the header information found in finalseg.csv. The output in finalseg.csv is organized into the following categories: 1) locus and alleles, 2) CSQ, 3) Global-Non-Affected, 4) Global-Affected, 5) Family, 6) Family-Affected, and 7) Family-Non-Affected. If you prefer not to include CSQ in the output file, you can select CSQ=False.
Step 4: Parsing
+To parse the file and remove unnecessary characters such as ", [, ], etc., run the following code.
+from segpy import parser
+parser.clean_general(outfolder)
+The information consists of various components; to eliminate redundancy, execute the following code.
+parser.clean_unique(outfolder)
+Step 5: Shut down spark
+Do not forget to deactivate environment and stop the spark:
+${SPARK_HOME}/sbin/stop-master.sh
+
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diff --git a/0.0.1/segpy_slurm/index.html b/0.0.1/segpy_slurm/index.html
new file mode 100644
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+
+ Segpy via slurm
+segpy.pip is a sheild designed for executing the segpy analysis on Linux workstation or HPC system utilizing the Slurm scheduler. Below, we illustrate the code to submit jobs for (steps 0 to 3) of the pipeline on an HPC system utilizing the Slurm scheduler. This pipeline has been employed on Beluga, an HPC system utilizing the Slurm system.
Contents
+ +The following flowchart illustrates the steps for running the segregation analysis on HPC system utilizing the Slurm system.
+ |
+
|---|
| segpy.svn workflow | +
To execute the pipeline, you require 1) the path of the pipeline (PIPELINE_HOME), 2) the working directory, 3) the VCF file, and 4) the PED file.
+export PIPELINE_HOME=~/segpy.slurm
+PWD=~/outfolder
+VCF=~/data/VEP_iPSC.vcf
+PED=~/data/iPSC_2.ped
+Step 0: Setup
+Initially, execute the following code to set up the pipeline. You can modify the parameters in ${PWD}/job_output/segpy.config.ini.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 0 \
+--mode slurm
+Step 1: Create table matrix
+The following code, create MatrixTable from the VCF file.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 1 \
+--vcf ${VCF}
+Step 2: Run segregation
+Execute the following code to generate the segregation.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 2 \
+--vcf ${VCF} \
+--ped ${PED}
+Step 3: Parsing
+To parse the file and remove unnecessary characters such as ", [, ], etc., run the following code.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 3 \
+--parser general
+The following code eliminates duplicate information in CSQ.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 3 \
+--parser unique
+Note
+You can execute steps 1 to 3 sequentially, as illustrated below.
+sh $PIPELINE_HOME/launch_pipeline.segpy.sh \
+-d ${PWD} \
+--steps 1-3 \
+--vcf ${VCF} \
+--ped ${PED} \
+--parser general
+
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+ « Previous
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+ Next »
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diff --git a/0.0.1/sitemap.xml b/0.0.1/sitemap.xml
new file mode 100644
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--- /dev/null
+++ b/0.0.1/sitemap.xml
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+ Tutorial
+This section contains tutorials for segregation analysis.
+segpy module
+segpy slurm
+segpy local
+container
+ +
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diff --git a/latest b/latest
index 4e379d2..8a9ecc2 120000
--- a/latest
+++ b/latest
@@ -1 +1 @@
-0.0.2
+0.0.1
\ No newline at end of file
diff --git a/versions.json b/versions.json
index 5928269..1da6d61 100644
--- a/versions.json
+++ b/versions.json
@@ -2,13 +2,13 @@
{
"version": "0.0.2",
"title": "0.0.2",
- "aliases": [
- "latest"
- ]
+ "aliases": []
},
{
"version": "0.0.1",
"title": "0.0.1",
- "aliases": []
+ "aliases": [
+ "latest"
+ ]
}
]