#motif #search-algorithms #fasta #algorithm #input-file #find #motifs

bin+lib motif_finder

Find motifs using Gibbs Sampler, Median String, and Randomized Motif Search algorithms in a fasta formatted file of reads Refer to the README to understand the input data

16 releases (6 breaking)

0.9.2 Jul 7, 2023
0.9.1 Apr 20, 2023
0.6.2 Apr 12, 2023
0.5.2 Apr 2, 2023
0.1.5 Mar 25, 2023

#66 in Biology

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GPL-3.0-only

49KB
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Motif Finder

Welcome to Motif Finder! This is a command line utility that allows you to take a FASTA file, specify a few parameters, and (hopefully) get some motifs prevalent in the sequences.

Installation

If you have the Rust toolchain installed, you can install motif_finder with:

cargo install motif_finder

If you do not have the Rust toolchain installed, you can install it here.

If you don't want to install it, you can also use the precompiled binaries in the releases tab on the right for your platform

If your platform isn't included, you can build it for your platform by cloning this repository:

git clone https://github.com/nithishbn/MotifFinder.git

and running

cargo build --release

in the source directory. This will leave an executable in the target/release/ directory which you can then run in the command line: motif_finder

Data format

This tool technically accepts all FASTA files, but the way it's meant to be used is to use an interesting approach in motif finding.

RNASeq

By using RNASeq data and aligning it back to a reference genome, we can identify the alignment sites of transcripts. Using these alignment sites, we can generate the set of sequences x bp upstream of the site in which to look for motifs, specifically for transcription factor binding sites.

This method involves finding an organism with RNASeq data, a reference genome, and a few bioinformatics tools including samtools, bamtools, and bedtools.

Examples

You can try to find the motifs present in promoters.fasta, a set of 4 promoters known in P. tricornutum, a relatively unknown diatom species.

De novo

Gibbs Sampler

Gibbs Sampler is an algorithm that iteratively searches for the best set of motifs in a set of sequences and throws out motifs at random until all iterations are finished.

motif_finder promoters.fasta -e 4 -k 10 -o promotifs.txt gibbs -t 100 -r 100

Randomized Motif Search is an algorithm that iteratively searches for the best set of motifs in a set of sequences and throws out motifs at random until the score cannot be improved anymore.

motif_finder promoters.fasta -e 4 -k 10 -o promotifs.txt randomized -r 100

Median String

Median String is an algorithm that checks the hamming distance from each kmer from each sequence and returns the minimized kmer from all strings. This algorithm is incredibly slow but can result in very accurate but short kmers. Be warned when using large k values.

motif_finder promoters.fasta -e 4 -k 8 -o promotifs.txt median

Find Motifs

Find Motif takes in an existing motif, an edit distance i.e. the max distance between motif and the sequence, and finds the positions throughout the entire input file where this match occurs. It will print the matches to the console.

motif_finder promoters.fasta -e 4 find_motif CTCAGCG 0 --quiet

Alignment

If you wish to align the motifs you've generated back to the sequences from which they were generated to identify the highest locally scored motif over all sequences, you can run the same commands as above but with the -a flag

motif_finder promoters.fasta -e 4 -k 8 -a -o promotifs.txt randomized -r 100

This will generate alignments for the motifs after identifying the motifs.

Other flags

verbosity - set verbosity with the --quiet or --verbose flags. --quiet offers some performance improvements in large input files and k values.

Dependencies

~19–30MB
~443K SLoC