[![stable](http://badges.github.io/stability-badges/dist/stable.svg)](http://github.com/badges/stability-badges) [![Build Status](https://travis-ci.org/incertae-sedis/smof.svg?branch=master)](https://travis-ci.org/incertae-sedis/smof) [![Docker Docker build](https://img.shields.io/docker/cloud/build/incertaesedis/smof.svg)](https://hub.docker.com/r/incertaesedis/smof/) [![docker pulls](https://img.shields.io/docker/pulls/incertaesedis/smof.svg)](https://hub.docker.com/r/incertaesedis/smof/) ![PyPI](https://img.shields.io/pypi/v/smof.svg) [![DOI](https://zenodo.org/badge/19203682.svg)](https://zenodo.org/badge/latestdoi/19203682) smof - Simple Manipulation Of FASTA ==== UNIX-style FASTA tools Installation ============ ``` pip install smof ``` Functions ========= `smof` is divided into the following subcommands: | subcommand | description | | ---------- | ----------------------------------------------------- | | `cut` | emulates UNIX cut command, where fields are entries | | `clean` | cleans fasta files | | `consensus` | finds the consensus sequence for aligned sequence | | `filter` | extracts sequences meeting the given conditions | | `grep` | roughly emulates the UNIX grep command | | `md5sum` | calculate an md5 checksum for the input sequences | | `head` | writes the first sequences in a file | | `permute` | randomly order sequence | | `reverse` | reverse each sequence (or reverse complement) | | `sample` | randomly select entries from fasta file | | `sniff` | extract info about the sequence | | `sort` | sort sequences | | `split` | split a fasta file into smaller files | | `stat` | calculate sequence statistics | | `subseq` | extract subsequence from each entry (revcomp if a\' aa.faa zzz* rm zzz* ``` Of you can split a large file into many smaller files with a set maximum number of sequences per file ```bash smof split -qn 500 -p zzz aa.faa grep -c '>' aa.faa zzz* rm zzz* ``` ## `smof uniq` This command corresponds roughly to GNU uniq, but entries are considered identical only if both header and sequence are exactly the same. As currently implemented, I don't find much use for this command. ## `smof wc` Outputs the number of characters and entries in the fasta file. Generally `smof stat` is better. ## `smof grep` Whereas GNU grep searches lines for matches, `smof grep` searches either the FASTA headers or the FASTA sequence. Extract the entries by matches to the header (default) ``` bash smof grep H312_03353 aa.faa ``` Extract entries by matches to a sequence ```bash smof grep --match-sequence SKSQ aa.faa # or equivalently smof grep -q SKSQ aa.faa ``` You can include flanking regions in the match ```bash # match 3 residues downstream smof grep -qA3 'SKSQ' aa.faa # match 3 residues upstream smof grep -qB3 'SKSQ' aa.faa # match 3 residues up- and downstream smof grep -qC3 'SKSQ' aa.faa ``` Inclusion of flanking regions is particularly useful in tandem with the -o option, which extracts only the matching sequence ```bash smof grep -qoA3 'SKSQ' aa.faa ``` Write the output in gff format ```bash smof grep -q --gff SKSQ aa.faa ``` You can count the number of sequences with a match ```bash smof grep -qc SKS aa.faa ``` Or the total number of matches ```bash smof grep -qm SKSQ aa.faa ``` Or both ```bash smof grep -qmc SKS aa.faa ``` Just like in GNU grep, you can invert a search. This search finds all genes that are not annotated as being hypothetical genes. ```bash smof grep -v hypothetical aa.faa ``` By default `smof grep` is case insensitive (unlike GNU grep), but it can be made case sensitive ```bash smof grep -I CoA aa.faa ``` You can search using patterns in a file ```bash smof grep -f id-sample.txt aa.faa ``` This, however, can be a little slow, since it searchs each pattern in the file against the entire header. A much faster approach is to extract a search pattern from the headers (or sequence) and then lookup the header pattern in the set of search patterns. ```bash smof grep -f id-sample.txt -w '\| (\S+) \|' aa.faa ``` Count occurrences (on both strands) of a DNA pattern using IUPAC extended nucleotide alphabet. ```bash smof grep -qmbG YYNCTATAWAWASM aa.supercontigs.fna ``` You can search using a sequence query ```bash # select 5 random sequences smof sample -n 5 aa.faa | smof subseq -b 5 35 > rand.faa smof grep -q --fastain rand.faa aa.faa ``` Or you can search for identical sequences shared between two fasta files ```bash smof sample -n 5 aa.faa > rand.faa smof grep -q --fastain rand.faa aa.faa ``` Find non-overlapping open reading frames of length greater than 100 codons. This is meant as an example of regex searching. This will NOT give you a great answer. smof does not consider frames (nor will it ever). It will not find the set of longest possible ORFs. If you want to identify ORFs, you should use a specialized program. That said: ``` bash smof grep -qPb --gff 'ATG(.{3}){99,}?(TAA|TGA|TAG)' aa.supercontigs.fna ``` ## `smof md5sum` This tool is useful if you want a checksum for a FASTA file that is independent of format (e.g. column width or case). String manipulation commands ============================ ## `smof permute` Permutes the letters of a sequence ## `smof reverse` Reverses a sequence (does NOT take the reverse complement) ## `smof subseq` ``` bash # extract a subsequence smof grep H312_00003T0 aa.faa | smof subseq -b 10 20 # color the subsequences instead smof grep H312_00003T0 aa.faa | smof subseq -b 10 20 -c red ``` If the start is higher than the end, and the sequence appears to be a DNA sequence, then smof will take the reverse complement. `smof subseq` can also read from a gff file. However, if you want to extract many sequences from a fasta file using a gff file as a guide (or other gff/bed manipulations), consider using a specialized tools such as `bedtools`. Biological sequence tools ========================= ## `smof clean` This command can be used to tidy a sequence. You can change the column width, remove gaps and stops, convert all letters to one case and/or change irregular characters to unknowns. By default, it removes whitespace in a sequence and makes uniform, 80-character columns. ## `smof filter` Output only sequence that meet a set of conditions. If you want to only keep sequences that are longer than 100 letters ```bash smof clean -x aa.faa | smof filter -l 100 ``` Note that I call clean before filtering to remove the stop character, which should not be included when calculating length. Or shorter than 100 letters ```bash smof clean -x aa.faa | smof filter -s 100 aa.faa ``` Or that have greater than 50% AFILMVW content (hydrophobic amino acids) ```bash smof clean -x aa.faa | smof filter -c 'AFILMVW > .5' aa.faa ``` ## `smof sniff` This command runs a number of checks on a FASTA file and is useful in diagnosing problems. For details, run `smof sniff -h`. ## `smof stat` The default operation outputs number of sequences and summary statistics concerning the sequence lengths. ```bash smof stat aa.supercontigs.fna nseq: 431 nchars: 12163397 5sum: 445 3301 9555 30563 746881 mean(sd): 28221 (58445) N50: 71704 ``` '5sum' refers to the five number summary of the sequence lengths (minimum, 25% quantile, median, 75% quantile, and maximum). Statistics can also be calculated on a sequence-by-sequence level, which by default outputs the sequence names (the first word of the header) and the sequence length, e.g. ```bash smof stat -q aa.supercontigs.fna | head ``` There are many other options. Run `smof stat -h` for descriptions. Case study: exploring motifs in chloroplast genomes =================================================== Alice is interested in the chloroplast *maturase* gene. Bob gives her a sample dataset which includes 10 fasta files of proteins encoded by the chloroplast genomes of 10 different plant species. These files are available in the `sample-data/chloroplasts` directory. You can find this dataset in the folder *doc/test-data/chloroplast-proteins*. Her first step is to explore the data. She first counts the sequences in each file with a simple grep command. ``` grep -c '>' *faa ``` Next she tests the sequences with `smof sniff` ``` smof sniff *faa ``` Producing the following output: ``` 578 uniq sequences (757 total) All prot All uppercase Protein Features: initial-Met: 755 99.7358% terminal-stop: 0 0.0000% internal-stop: 0 0.0000% selenocysteine: 0 0.0000% Universal Features: unknown: 8 1.0568% ambiguous: 0 0.0000% gapped: 0 0.0000% ``` Everything looks pretty good. But two of the sequences don't start with a methionine. Alice wants to find them. She does this using `smof grep` and a Perl regular expressions. ``` smof grep -qP '^[^M]' *faa ``` She finds these genes are both from *Solanum lycopersicum* and are described in the fasta headers as being *partial*. Now Alice wants to find the *maturase* genes by pulling out every entry with 'maturase' in the fasta header. ``` smof grep maturase *faa smof grep maturase *faa > maturase.faa ``` For a close look at the distribution of sequence lengths, Alice calls `smof stat` ``` smof stat maturase.faa ``` Alice happens to be interested in the sequence WTQPQR from *Panicum virgatum* and would like to know what the homologous regions are in the other species. So Alice aligns the maturase genes with [MUSCLE](http://nar.oxfordjournals.org/content/32/5/1792.short) and searches for the motif using the GFF output option. ``` muscle -quiet < maturase.faa | tee maturase.aln | smof grep -q --gff WTQPQR ``` This is outputs the location of the match in standard GFF format, i.e. the match is at position 329 to 334. Homologs to this sequence will be at the same positions in the aligned fasta file output by MUSCLE. ``` smof subseq -b 329 334 maturase.aln ``` HMMER could then be used to analyze the by-site conservation of the sextuplet.