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https://github.com/incertae-sedis/smof

Explore and analyze biological sequence data
https://github.com/incertae-sedis/smof

bioinformatics biology fasta genomics sequence sequence-analysis

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Explore and analyze biological sequence data

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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.