Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. This tool generate several features for next generation sequencing reads: such as log likelihood ratio of estimated true counts, error probability and observed count of the reads. The results then can be used for predicting true sequences from NGS data. Typically the output can be fed to Support Vector Machine (SVM) classifier. In our study (see paper below) we showed that on simulated reads these features can achieve 96.35% classification accuracy in discriminating true sequences. Using this framework we provide a way for users to select sequences with a desired precision and recall for their analysis.
For the program to generate full features, it requires
Boost (http://www.boost.org) to be installed in your path.
This is necessary for compiling Expectation Matching.
Type make in the /src directory.
and
Type ./compile_all.sh in both ematch_src/ and knapsack_src/ directory.
The code 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.
You can run recount by executing the wrapper written in Perl. The command is simply:
perl ngsfeatgen.pl <input>
For example:
perl ngsfeatgen.pl small-len10-50.txt
E.Wijaya, J-F Pessiot, M. C. Frith, W. Fujibuchi, K. Asai and P. Horton, In Search of True Reads: A Classification Approach to Next Generation Sequencing Data Selection, in Proc. 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Next Generation Sequencing Workshop, 561-566. (IEEE)
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