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Human Data

Raw reads were downloaded from BaseSpace under the sample "NA12878-L7" of project "HiSeq X Ten: TruSeq Nano (4 replicates of NA12878)". There are 444,764,118 pairs of reads, averaged 150bp or so in length. The raw data are kept in two gzip'd files, one file for each read end. As some programs work with one input file only or do not recognize gzip'd input files, we may decompress the raw data or create one interleaved FASTQ.

Hardware and Software

Fiona, BBMap and Bloocoo provide precompiled binaries. We compiled the rest of tools on a CentOS5 virtual machine. The executables are available here. We ran all the tools on a CentOS6 machine with 20 cores of Intel E5-2660 CPUs at 2.2GHz and 128GB RAM, and measured timing and peak memory with GNU time.

Error Correction Command Lines

# BBMap-34.38 (adding ecclimit=14 made little difference)
ecc.sh -Xmx32g in=read1.fastq.gz in2=read2.fastq.gz out=ec.fq tmpdir=tmp threads=16 ecc=t aec=t k=31

# BFC-r175
bash -c "bfc -s 3g -k55 -t 16 <(seqtk mergepe read1.fq.gz read2.fq.gz) <(seqtk mergepe read1.fq.gz read2.fq.gz) | gzip -1 > ec.fq.gz"

# BFC-kmc
echo -e "read1.fq.gz\nread2.fq.gz" > list.txt
kmc -k55 -m24 @list.txt reads.k55 tmp
seqtk mergepe read1.fq.gz read2.fq.gz | bfc-kmc -t16 reads.k55 | gzip -1 > ec.fq.gz

# BLESS-v0p23 (much faster than v0p17 or earlier)
bless -read1 read1.fq -read2 read2.fq -kmerlength 55 -prefix out -smpthread 16 -max_mem 24 -notrim

# Bloocoo-1.0.4
Bloocoo -nb-cores 16 -file read12.fq.gz -kmer-size 31

# Fermi2-r175; ropebwt2-r187
seqtk mergepe read1.fq.gz read2.fq.gz | ropebwt2 -drq20 -x31 > index.fmd
seqtk mergepe read1.fq.gz read2.fq.gz | fermi2 correct -t 16 -k 29 index.fmd /dev/stdin | gzip -1 > ec.fq.gz

# Fiona-0.2.0 (killed due to large memory footprint)
fiona -g 3000000000 --sequencing-technology illumina --no-final-trim-ns -nt 16 read12.fq ec.fq

# Lighter-20150123
lighter -K 31 3000000000 -r read1.fq.gz -r read2.fq.gz -t 16

# Musket-1.1
musket -k 27 3000000000 -p 16 -inorder -o ec.fq read12.fq

# QuorUM-1.0.0
quorum -s 12000000000 -t 16 -p quorum-k31 -k 31 read1.fq read2.fq

# SGA-0.9.13
sga preprocess -p 1 read1.fq.gz read2.fq.gz | gzip -1 > out.pe.fq.gz
sga index -a ropebwt -t 16 --no-reverse out.pe.fq.gz
sga correct -t 16 -k 55 --learn out.pe.fq.gz

# Trowel-0.1.4.1 (killed due to large memory footprint)
echo "read1.fq read2.fq" > list.txt
trowel -t 16 -f list.txt -k 31 -ntr

Assembly-related Command Lines

# Velvet-1.2.10 assembly
velveth k61 61 -shortPaired -fmtAuto -interleaved SRR065390.fq
velvetg k61 -exp_cov auto -scaffolding yes -cov_cutoff auto -ins_length 250

# ABySS-1.5.2 assembly
abyss-pe name=k67 k=67 in=SRR065390.fq q=0 s=500 n=5

# Fermikit-0.9 assembly (option -E to skip error correction)
fermi2.pl unitig -Es100m -t16 SRR065390.fq > SRR065390.mak; make -f SRR065390.mak

# Contig-to-reference mapping (reference version 235)
bwa mem -x intractg -t 8 celegans.fa contigs.mag.gz > contigs.sam

# Aligned-N50 and break points (htsbox binary included in fermikit-0.9)
htsbox abreak -l200 contigs.sam

# Variant calling from short reads with freebayes-0.9.20
freebayes --experimental-gls -f celegans.fa reads.bam

# Variant calling from contigs
htsbox pileup -cuf celegans.fa contigs.bam > contigs.vcf

# Variant filtering
k8 hapdip.js deovlp contigs.vcf | k8 hapdip.js anno > contigs.anno
k8 hapdip.js filter -DF36 -q3 contigs.anno > contigs.flt

# Comparison to freebayes calls (all.bed is a BED including whole genome regions)
k8 hapdip.js distEval -b all.bed freebayes.raw.vcf contigs.vcf

Comments

  1. Early versions of BLESS were single-threaded and slow (Molnar and Ilie, 2014), and did not work with unequal read lengths. More recent versions are much better.

  2. Lighter-1.0.4 is slow on gzip'd input, so I raised issues #13 and #14. The version checked out on 2015-01-23 and compiled with the latest zlib is much more performant on gzip'd input.

  3. In addition to tools shown in the table, we have also tried Trowel-0.1.4.1, Fiona-0.2.0 and AllPathsLG-51828. They were taking over 110GB RAM and got manually killed. Coral, HiTEC and SHREC are unable to work with data at this scale according to a few publications. RACER requires more RAM than the total read bases (>120GB) according to the developers. These tools are not tested.

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