typos and grammar in the manual

docs/datatypes.md

@@ -4,11 +4,11 @@
 
 Only FASTQ or BAM files are supported as input.
 
-The selection of k-mer length is non-trivial for IonTorrent. If the dataset is more or less conventional (good coverage, moderate or low GC, etc), then use our [recommendation for long reads](datatypes.md#assembling-long-illumina-paired-reads) (e.g. assemble using k-mer lengths 21,33,55,77,99,127). However, due to increased error rate some changes of k-mer lengths (e.g. selection of shorter ones) may be required. For example, if you ran SPAdes with k-mer lengths 21,33,55,77 and then decided to assemble the same data set using more iterations and larger values of K, you can run SPAdes once again specifying the same output folder and the following options: `--restart-from k77 -k 21,33,55,77,99,127 --mismatch-correction -o <previous_output_dir>`. Do not forget to copy contigs and scaffolds from the previous run. We are planning to tackle issue of selecting k-mer lengths for IonTorrent reads in next versions.
+The selection of k-mer length is non-trivial for IonTorrent. If the dataset is more or less conventional (good coverage, moderate or low GC, etc), then use our [recommendation for long reads](datatypes.md#assembling-long-illumina-paired-reads) (e.g. assemble using k-mer lengths 21,33,55,77,99,127). However, due to increased error rate some changes of k-mer lengths (e.g. selection of shorter ones) may be required. For example, if you ran SPAdes with k-mer lengths 21,33,55,77 and then decided to assemble the same data set using more iterations and larger values of K, you can run SPAdes once again specifying the same output folder and the following options: `--restart-from k77 -k 21,33,55,77,99,127 --mismatch-correction -o <previous_output_dir>`. Do not forget to copy contigs and scaffolds from the previous run.
 
 You may need no error correction for Hi-Q enzyme at all. However, we suggest trying to assemble your data with and without error correction and select the best variant.
 
-For non-trivial datasets (e.g. with high GC, low or uneven coverage) we suggest to enable single-cell mode (setting `--sc` option) and use k-mer lengths of 21,33,55.
+For non-trivial datasets (e.g. with high GC, low or uneven coverage) we suggest enabling single-cell mode (setting `--sc` option) and use k-mer lengths of 21,33,55.
 
 ## Assembling long Illumina paired reads
 
@@ -24,7 +24,7 @@ Do not turn off SPAdes error correction (BayesHammer module), which is included
 
 If you have enough coverage (50x+), then you may want to try to set k-mer lengths of 21, 33, 55, 77 (selected by default for reads with length 150bp).
 
-Make sure you run assembler with the `--careful` option to minimize number of mismatches in the final contigs.
+Make sure you run assembler with the `--careful` option to minimize the number of mismatches in the final contigs.
 
 We recommend that you check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.
 
@@ -46,11 +46,11 @@ To correct and assemble the reads:
 
 Do not turn off SPAdes error correction (BayesHammer module), which is included in SPAdes default pipeline.
 
-By default we suggest to increase k-mer lengths in increments of 22 until the k-mer length reaches 127. The exact length of the k-mer depends on the coverage: k-mer length of 127 corresponds to 50x k-mer coverage and higher. For read length 250bp SPAdes automatically chooses K values equal to 21, 33, 55, 77, 99, 127.
+By default we suggest increasing k-mer lengths in increments of 22 until the k-mer length reaches 127. The exact length of the k-mer depends on the coverage: k-mer length of 127 corresponds to 50x k-mer coverage and higher. For read length 250bp SPAdes automatically chooses K values equal to 21, 33, 55, 77, 99, 127.
 
-Make sure you run assembler with `--careful` option to minimize number of mismatches in the final contigs.
+Make sure you run assembler with `--careful` option to minimize the number of mismatches in the final contigs.
 
-We recommend you to check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.
+We recommend you to check the SPAdes log file at the end of each iteration to control the average coverage of the contigs.
 
 For reads corrected prior to running the assembler:
 

docs/getting-started.md

@@ -1,10 +1,10 @@
 # Quick start
 
-- SPAdes is an assembler for second-generation sequencing data (Illumina or IonTorrent). PacBio and Nanopore reads are supported *only* as supplementary data. SPAdes can assemble genomes, metagenomes, transcriptomes, viral geonmes etc. 
+- SPAdes is an assembler for second-generation sequencing data (Illumina or IonTorrent). PacBio and Nanopore reads are supported *only* as supplementary data. SPAdes can assemble genomes, metagenomes, transcriptomes, viral genomes etc.
 
 - Download SPAdes binaries for [Linux](https://github.com/ablab/spades/releases/download/v3.15.5/SPAdes-3.15.5-Linux.tar.gz) or [MacOS](https://github.com/ablab/spades/releases/download/v3.15.5/SPAdes-3.15.5-Darwin.tar.gz). You can also compile SPAdes from [source](https://github.com/ablab/spades/releases/download/v3.15.5/SPAdes-3.15.5.tar.gz) (requires g++ 9.0+, cmake 3.16+, zlib and libbz2). SPAdes requires only Python 3.8+ to be installed.
 
-- Test your SPAdes intallation by running
+- Test your SPAdes installation by running
 
 ```
     bin/spades.py --test

docs/hmm.md

@@ -12,10 +12,11 @@ Given an increased interest in coronavirus research we developed a coronavirus a
 
 ## wastewaterSPAdes mode
 
-SARS-CoV-2 wastewater samples are extensively collected and studied because it allows quantitative assessment of viral load in surrounding populations. We developed wastewaterSPAdes that solves SARS-CoV-2 deconvolution problem using assembly graph structure.
+SARS-CoV-2 wastewater samples are extensively collected and studied because it allows quantitative assessment of viral load in surrounding populations. We developed wastewaterSPAdes that solves the SARS-CoV-2 deconvolution problem using assembly graph structure.
 To use wastewaterSPAdes, you'll need to:
 
 - Set `--sewage` flag to the `coronaspades.py`.
 - Provide the SARS-CoV-2 reference genome as trusted contigs.
 
-Results of wastewaterSPAdes are stored in `lineages.csv` file. First column contains strain name, and second column contains estimated abundance of this strain in the sample.
+Results of wastewaterSPAdes are stored in `lineages.csv` file. First column contains the strain name, and second column contains estimated abundance of this strain in the sample.
+

docs/hybrid.md

@@ -6,13 +6,13 @@ SPAdes can take as an input an unlimited number of PacBio and Oxford Nanopore li
 
 PacBio CLR and Oxford Nanopore reads are used for hybrid assemblies (e.g. with Illumina or IonTorrent). There is no need to pre-correct this kind of data. SPAdes will use PacBio CLR and Oxford Nanopore reads for gap closure and repeat resolution.
 
-For PacBio you just need to have filtered subreads in FASTQ/FASTA format. Provide these filtered subreads using `--pacbio` option. Oxford Nanopore reads are provided with `--nanopore` option.
+For PacBio you just need to have filtered subreads in FASTQ/FASTA format. Provide these filtered subreads using the `--pacbio` option. Oxford Nanopore reads are provided with `--nanopore` option.
 
 PacBio CCS/Reads of Insert reads or pre-corrected (using third-party software) PacBio CLR / Oxford Nanopore reads can be simply provided as single reads to SPAdes.
 
 ## Additional contigs
 
-In case you have contigs of the same genome generated by other assembler(s) and you wish to merge them into SPAdes assembly, you can specify additional contigs using `--trusted-contigs` or `--untrusted-contigs`. First option is used when high quality contigs are available. These contigs will be used for graph construction, gap closure and repeat resolution. Second option is used for less reliable contigs that may have more errors or contigs of unknown quality. These contigs will be used only for gap closure and repeat resolution. The number of additional contigs is unlimited.
+In case you have contigs of the same genome generated by another assembler(s) and you wish to merge them into SPAdes assembly, you can specify additional contigs using `--trusted-contigs` or `--untrusted-contigs`. First option is used when high quality contigs are available. These contigs will be used for graph construction, gap closure and repeat resolution. Second option is used for less reliable contigs that may have more errors or contigs of unknown quality. These contigs will be used only for gap closure and repeat resolution. The number of additional contigs is unlimited.
 
 Note, that SPAdes does not perform assembly using genomes of closely-related species. Only contigs of the same genome should be specified.
 

docs/input.md

@@ -10,7 +10,7 @@ To run SPAdes you need at least one library of the following types:
 
 Illumina and IonTorrent libraries should not be assembled together. All other types of input data are compatible. SPAdes should not be used if only PacBio CLR, Oxford Nanopore, Sanger reads or additional contigs are available.
 
-SPAdes supports mate-pair only assembly. However, we recommend to use only high-quality mate-pair libraries in this case (e.g. that do not have a paired-end part). We tested mate-pair only pipeline using Illumina Nextera mate-pairs. See more [here](running.md#specifying-multiple-libraries).
+SPAdes supports mate-pair only assembly. However, we recommend to use only high-quality mate-pair libraries in this case (e.g. that do not have a paired-end part). We tested the mate-pair-only pipeline using Illumina Nextera mate-pairs. See more [here](running.md#specifying-multiple-libraries).
 
 Notes:
 
@@ -43,10 +43,9 @@ In an unlikely case some of the reads from your mate-pair (or high-quality mate-
 
 ## Unpaired (single-read) libraries
 
-By using command line interface, you can specify up to nine different single-read libraries. To input more libraries, you can use [YAML data set file](running.md#specifying-multiple-libraries-with-yaml-data-set-file).
+By using the command line interface, you can specify up to nine different single-read libraries. To input more libraries, you can use [YAML data set file](running.md#specifying-multiple-libraries-with-yaml-data-set-file).
 
-Single librairies are assumed to have high quality and a reasonable coverage. For example, you can provide PacBio CCS reads as a single-read library.
+Single libraries are assumed to have high quality and reasonable coverage. For example, you can provide PacBio CCS reads as a single-read library.
 
 Note, that you should not specify PacBio CLR, Sanger reads or additional contigs as single-read libraries, each of them has a separate [option](running.md#input-data).
 
-

docs/installation.md

@@ -113,7 +113,7 @@ If you added `bin` folder from SPAdes installation directory to the `PATH` varia
     spades.py --test
 ```
 
-For the simplicity we further assume that `bin` folder from SPAdes installation directory is added to the `PATH` variable.
+For simplicity we further assume that the `bin` folder from SPAdes installation directory is added to the `PATH` variable.
 
 If the installation is successful, you will find the following information at the end of the log:
 

docs/output.md

@@ -16,7 +16,7 @@ Contigs/scaffolds names in SPAdes output FASTA files have the following format:
 `>NODE_3_length_237403_cov_243.207`
 Here `3` is the number of the contig/scaffold, `237403` is the sequence length in nucleotides and `243.207` is the k-mer coverage for the last (largest) k value used. Note that the k-mer coverage is always lower than the read (per-base) coverage.
 
-In general, SPAdes uses two techniques for joining contigs into scaffolds. First one relies on read pairs and tries to estimate the size of the gap separating contigs. The second one relies on the assembly graph: e.g. if two contigs are separated by a complex tandem repeat, that cannot be resolved exactly, contigs are joined into scaffold with a fixed gap size of 100 bp. Contigs produced by SPAdes do not contain N symbols.
+In general, SPAdes uses two techniques for joining contigs into scaffolds. First one relies on read pairs and tries to estimate the size of the gap separating contigs. The second one relies on the assembly graph: e.g. if two contigs are separated by a complex tandem repeat that cannot be resolved exactly, contigs are joined into a scaffold with a fixed gap size of 100 bp. Contigs produced by SPAdes do not contain N symbols.
 
 ## Assembly graph formats
 
@@ -83,10 +83,10 @@ For all plasmidSPAdes' contig names in `contigs.fasta`, `scaffolds.fasta` and `a
 
 
 ## metaplasmidSPAdes and metaviralSPAdes output
-The repeat resolution and extrachromosomal element detection in metaplasmidSPAdes/metaviralSPAdes is run independently for different coverage cutoffs values (see [paper](https://genome.cshlp.org/content/29/6/961.short) for details). In order to distinguish contigs with putative plasmids detected at different cutoff levels we extend the contig name in FASTA file with cutoff value used for this particular contig (in format `_cutoff_N`). This is why, in the contrast to regular SPAdes pipeline, there might be a contig with `NODE_1_` prefix for each cutoff with potential plasmids detected. In following example, there were detected two potential viruses using cutoff 0, one virus was detected with cutoff 5 and one with cutoff 10.
-Also, we add a suffix that shows the structure of the suspective extrachromosomal element.
-For metaplasmid mode we output only circular putative plasmids.
-For metaviral mode we also output linear putative viruses and linear viruses with simple repeats ('9'-shaped components in the assembly graph) sequences.
+The repeat resolution and extrachromosomal element detection in metaplasmidSPAdes/metaviralSPAdes is run independently for different coverage cutoffs values (see [paper](https://genome.cshlp.org/content/29/6/961.short) for details). In order to distinguish contigs with putative plasmids detected at different cutoff levels we extend the contig name in FASTA file with cutoff value used for this particular contig (in format `_cutoff_N`). This is why, in the contrast to regular SPAdes pipeline, there might be a contig with `NODE_1_` prefix for each cutoff with potential plasmids detected. In the following example, there were detected two potential viruses using cutoff 0, one virus was detected with cutoff 5 and one with cutoff 10. We also add a suffix that shows the structure of the suspective extrachromosomal element.
+
+In the metaplasmid mode SPAdes outputs only circular putative plasmids.
+In the metaviral mode SPAdes also outputs linear putative viruses and linear viruses with simple repeats ('9'-shaped components in the assembly graph) sequences.
 
 ``` 
 >NODE_1_length_40003_cov_13.48_cutoff_0_type_circular
@@ -98,15 +98,17 @@ For metaviral mode we also output linear putative viruses and linear viruses wit
 ## biosyntheticSPAdes output
 
 biosyntheticSPAdes outputs four files of interest:
-- scaffolds.fasta – contains DNA sequences from putative biosynthetic gene clusters (BGC). Since each sample may contain multiple BGCs and biosyntheticSPAdes can output several putative DNA sequences for eash cluster, for each contig name we append suffix `_cluster_X_candidate_Y`, where X is the id of the BGC and Y is the id of the candidate from the BGC.
-- raw_scaffolds.fasta – SPAdes scaffolds generated without domain-graph related algorithms. Very close to regular scaffolds.fasta file.
-- hmm_statistics.txt – contains statistics about BGC composition in the sample. First, it outputs number of domain hits in the sample. Then, for each BGC candidate we output domain order with positions on the corresponding DNA sequence from scaffolds.fasta.
-- domain_graph.dot – contains domain graph structure, that can be used to assess complexity of the sample and structure of BGCs. For more information about domain graph construction, please refer to the paper.
+- scaffolds.fasta – contains DNA sequences from putative biosynthetic gene clusters (BGC). Since each sample may contain multiple BGCs and biosyntheticSPAdes can output several putative DNA sequences for each cluster, for each contig name we append suffix `_cluster_X_candidate_Y`, where X is the id of the BGC and Y is the id of the candidate from the BGC.
+- raw_scaffolds.fasta - SPAdes scaffolds generated without domain-graph related algorithms. Very close to the regular scaffolds.fasta file.
+- hmm_statistics.txt - contains statistics about BGC composition in the sample. First, it outputs the number of domain hits in the sample. Then, for each BGC candidate we output domain order with positions on the corresponding DNA sequence from scaffolds.fasta.
+- domain_graph.dot - contains domain graph structure that can be used to assess complexity of the sample and structure of BGCs. For more information about domain graph construction, please refer to the paper.
+
 
 ## rnaSPades output
 
 See [rnaSPAdes section](rna.md#rnaspades-output).
 
+
 ## Genome assembly evaluation
 
 [QUAST](https://quast.sourceforge.net/) may be used to generate summary statistics (N50, maximum contig length, GC %, \# genes found in a reference list or with built-in gene finding tools, etc.) for a single assembly. It may also be used to compare statistics for multiple assemblies of the same data set (e.g., SPAdes run with different parameters, or several different assemblers).