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<b)
`tail`	writes the last sequences in a file
`translate`	translate a DNA sequence into a protein sequence
`uniq`	count, omit, or merge repeated entries
`wc`	roughly emulates the UNIX wc command

Detailed instructions on how to use each command in smof is available via the '-h' option.

To list subcommands

smof -h

Get help on a specific subcommand

smof grep -h

Sample data

The FASTA files used in the examples below are available in the sample-data/anncaliia_algerae folder in the smof github repo (here).

UNIX-like commands

This group of subcommands include commands based off UNIX builtins.

`smof head` and `tail`

These functions mimic their GNU counterparts but on the entry, rather than line, level. For example smof head prints the first entry in a file and smof -5 prints the first 5. Similarly for smof tail.

smof head aa.faa
smof head -3 aa.faa
smof tail aa.faa
smof tail -3 aa.faa
smof tail +2 aa.faa | smof head

In addition to the GNU-like functionallity, smof head and tail can also limit the sequence that is output. This can be useful for diagnostic purposes.

# print last 3 nucleotides (last codon) from the first 5 transcripts
smof head -l 3 -5 aa.transcripts.fna
# print the first codon
smof head -f 3 -5 aa.transcripts.fna
# print first and last
smof head -f 3 -l 3 -5 aa.transcripts.fna

This sort of diagnostics is easier done with smof sniff.

`smof sort`

smof sort can be used to simply sort sequences alphabetically by header. It can also sort by sequence length. One useful feature with no homolog in GNU sort is the ability to sort by regex capture. For example, if the FASTA headers are formated like 'locus|xxx|taxon|yyy|gi|zzz', you can sort them numerically by taxon with the command smof sort -nx 'taxon\|(\d+)'.

# print the shortest sequence
smof sort -l aa.faa | smof head
# print the longest sequence
smof sort -l aa.faa | smof tail
# sort by the function in the header description
smof sort -x 'PRA339 (.*)' aa.faa | smof tail

`smof sample`

smof sample allows extraction of a random sample of entries. With no arguments, it reads the entire file into memory and outputs a random one.

# retrieve 1 sequence by default
smof sample aa.faa
smof sample -n 5 aa.faa
# set a random seed (useful for debugging and reproducible scripts)
smof sample --seed 42 aa.faa

`smof split`

This command allows easily splitting of a large file into many smaller files.

You can split a large file several small files with equal numbers of sequences

smof split -n 5 -p zzz aa.faa
grep -c '>' 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

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)

smof grep H312_03353 aa.faa

Extract entries by matches to a sequence

smof grep --match-sequence SKSQ aa.faa
# or equivalently
smof grep -q SKSQ aa.faa

You can include flanking regions in the match

# 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

smof grep -qoA3 'SKSQ' aa.faa

Write the output in gff format

smof grep -q --gff SKSQ aa.faa

You can count the number of sequences with a match

smof grep -qc SKS aa.faa

Or the total number of matches

smof grep -qm SKSQ aa.faa

Or both

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.

smof grep -v hypothetical aa.faa

By default smof grep is case insensitive (unlike GNU grep), but it can be made case sensitive

smof grep -I CoA aa.faa

You can search using patterns in a file

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.

smof grep -f id-sample.txt -w '\| (\S+) \|' aa.faa

Count occurrences (on both strands) of a DNA pattern using IUPAC extended nucleotide alphabet.

smof grep -qmbG YYNCTATAWAWASM aa.supercontigs.fna

You can search using a sequence query

# 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

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:

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`

# 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

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

smof clean -x aa.faa | smof filter -s 100 aa.faa

Or that have greater than 50% AFILMVW content (hydrophobic amino acids)

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.

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.

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 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.

incertae-sedis/smof

smof - Simple Manipulation Of FASTA

Installation

Functions

Sample data

UNIX-like commands

smof head and tail

smof sort

smof sample

smof split

smof uniq

smof wc

smof grep

smof md5sum