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RECOUNT: Probabilistic Error Correction for Next Generation Sequencing Data

RECOUNT is a software for estimating the true count of Illumina reads based on a probabilistic model. It uses the quality score provided by Illumina and the reads as its input. Typical application of this software is for transcriptome or metagenomic expression analysis.

Requirement

To handle around 20 million reads of length 34, using 1 Hamming distance neighbors you will need at least 10 gigabytes of disk space. To install the software you need a C++ compiler.

Installation

Type make in the /src directory. It should make two groups of programs:

  1. Components of RECOUNT: FindNeighboursWithQual, GenerateProportion and EstimateTrueCount.
  2. Additional preprocessing programs: AverageTagsQuals_27, AverageTagsQuals_36 and PickBaseQual

Input format

RECOUNT takes a pre-processed data as input. It looks like this:

700218	AAA     40	40	40	
25078	AAC     40	40	3	
25010	AAG     40	40	3	
25315	AAT     40	40	3	
25045	ACA     40	3	40

First column is the observed/actual count of a read, second colum is the read, and third column to the end is the average quality score of each bases in the corresponding read.

Usage

You can run recount by executing the wrapper written in Perl. The command is simply:

perl recount.pl [input] [no_of_neighbor_mismatch]

For example:

perl recount.pl test-data.txt 1

Maximum allowable number of mismatch is 2. Note that the running time and space requirement of RECOUNT using mismatch 2 is quadratic compare to using mismatch 1. For 2 mismatches option, you may reduce the space and running time requirement by setting the error error probability bar under a variable in recount.pl.

Additional data pre-processing softwares

Illumina's pipeline generated two types of data: sequence (seq) and quality score (prb). In this package we provide several types of additional softwares.

a. AverageTagsQuals_seq_prb takes the input of the following format:

AAA  40 -40 -40 -40     40 -40 -40 -40     40 -40 -40 -40
AAA  30 -40 -40 -40     10 -40 -40 -40     20 -40 -40 -40
AAA  20 -40 -40 -40     20 -40 -40 -40     40 -40 -40 -40
AAC  40 -40 -40 -40     40 -40 -40 -40     -40 -40 -40 40

then compute the average of the quality score. Note that the above input must be sorted by the reads ascendingly. And the output is:

3 AAA  30 -40 -40 -40     23.33 -40 -40 -40     33.33 -40 -40 -40
1 AAC  40 -40 -40 -40     40 -40 -40 -40     -40 -40 -40 40

The command is simply:

./AverageTagsQuals [sorted_seq_prb_file]

b. PickBaseQual takes the output of AverageTagsQuals above and produce the following result:

3 AAA 30 23.33 33.33
1 AAC 40 40 -40

The command is simply:

./PickBaseQual [average_prb_file]

Sometimes the data does not come in PRB format, but in FASTQ where the each base in the tag has only one corresponding quality score. For that reason we also provide another version of AverageTagsQuals_seq-prb which only average out based on single quality value of each base in the tag.

c. AverageTagsQuals takes the input of the following format:

AAA  40 40 40 
AAA  30 10 20 
AAA  20 20 40 
AAC  40 40 40

We call this SEQ-QUAL format. And the output is:

3 AAA  30  23.33 33.33 
1 AAC  40  40 40

Scripts

We also provided two scripts for converting FASTQ to FASTA format and FASTQ to SEQ-QUAL format.

  ./fastq2fasta.sh [fastq_file]

and

  perl fastq2seqprb.pl [fastq_file]

Clustering Reads By Length

After the FASTAQ format has been converted to SEQ-PRB format we need to separate the reads into several files according to its length. This can be done with the given script.

perl cluster_seqprb_by_taglen.pl [seq-prb-file]

It will then create several files with name: [seq-prb-file-LEN]

And next we need to create the average version of the reads

./AverageTagQuals_LEN [seq-prb-file-LEN]  > [seq-prb-file-LEN-avg]

Finally we can run Recount at the final output

perl recount.pl [seq-prb-file-LEN-avg]

Publication

Edward Wijaya, Martin C. Frith, Yutaka Suzuki, Paul Horton, Recount: expectation maximization based error correction tool for next generation sequencing data, (2009) Genome Inform. 23(1):189-201. PMID:20180274.

Credits

RECOUNT is developed in C++ by Edward Wijaya in the Computational Biology Research Center (CBRC) - AIST. The EM algorithm is based on (Beissbarth. et.al Bioinformatics (20), i31-39, 2007).

Questions, Comments, Problems

Email: [email protected]; [email protected] or [email protected]. If reporting the problem, please describe exactly how to trigger the problem.