gkmQC: gapped k-mer-SVM quality check and optimization

gkmQC is a sequence-based quality assessment and refinement of chromatin accessibility data using gkm-SVM. It trains a support vector classifier (SVC) using gapped-kmer kernels (Ghandi et al., 2014; Lee, 2016), and learns sequence features that modulate gene expressions. We use LIBSVM (Chang & Lin 2011) for implementing SVC.

requires

• Python >=3
• numpy
• sklearn
• bitarray
• pyfasta

Set conda virtual environment

$ conda env create -f environment.yml
$ conda activate gkmqc

Please compile C library for gkm-kernel

$ cd src
$ make && make install

To prepare null-seq index,
(1) download precalculated one:
gkmqc.idx.hg38.tar.xz (5.8 GB), gkmqc.idx.mm10.tar.xz (4.5 GB)

$ cd data
$ tar xvfJ gkmqc.idx.hg38.tar.xz

(2) or build your own null-seq index with chromFa.tar.gz file:
hg38.chromFa.tar.gz (938 MB), mm10.chromFa.tar.gz (830 MB)

$ cd data
# run buildindx command; takes 15 mins with 10 threads
$ ../bin/gkmqc.py buildidx -i hg38.chromFa.tar.gz -g hg38 -@ [threads]

Evaluate your called peaks and check your gkmQC curve.

# run evaluate command; takes 1 ~ 2 hrs with 10 threads
$ cd test
$ ../bin/gkmqc.py evaluate -i foo.narrowPeak -g hg38 -n foo -@ [threads]
$ cat foo.gkmqc/foo.gkmqc.eval.out
$ ../bin/gkmqc.py report -i foo.gkmqc/foo.gkmqc.eval.out
INFO: report gkmQC scores and curves
INFO: gkmQC score = 100.000
INFO: Curve PDF file has been created: ./test/foo.gkmqc.curve.pdf

Especially, gkmQC enables us to optimize open-chromatin peaks for rare cell-types in single-cell ATAC-seq data.

Based on the gkmQC-AUC of peak subsets called from original and relaxed threshold of MACS2, gkmQC optimizes the threshold of peak-calling. Consequently, gkmQC recovers uncalled peaks due to the insufficient reads mapped to rare cell-types.

Optimize your called peaks with gkmQC AUC scores.

# run optimize command;
# requires gkmQC results of called peaks with original and relaxed cut-off
# foo, foo_rc: prefixs of gkmQC result with peaks from either original, relaxed cut-off
$ cd test
$ ../bin/gkmqc.py optimize -p1 foo -p2 foo_rc
$ cat foo.gkmqc/foo.e300.optz.bed

You can check the options with -h arg of gkmqc.py

$ cd bin
$ ./gkmqc.py -h
$ ./gkmqc.py buildidx -h # Building null-seq index
$ ./gkmqc.py evaluate -h # run gkm-SVM to evaluate peaks
$ ./gkmqc.py optimize -h # run gkmQC to optimize peaks

Please cite below papers

• Han SK, Muto Y, Wilson PC, Chakravarti A, Humphreys BD, Sampson MG, Lee D. Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model (Submitted)
• Ghandi M†, Lee D†, Mohammad-Noori M, & Beer MA. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features. PLoS Comput Biol 10, e1003711 (2014). doi:10.1371/journal.pcbi.1003711 † Co-first authors
• Lee D. LS-GKM: A new gkm-SVM for large-scale Datasets. Bioinformatics btw142 (2016). doi:10.1093/bioinformatics/btw142
• Chang C.-C and Lin C.-J. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.