Gonetics
Go / Golang Bioinformatics Library
Install / Use
/learn @pbenner/GoneticsREADME
Gonetics
Gonetics is a bioinformatics library for the Go programming language (golang). It provides native data structures for handling genetic data and methods for handling common file formats such as BAM, GTF, BED, BigWig, and Wig. The documentation is available here.
Tools
Executables are available here.
| Tool | Description |
| ------------------------ | ------------------------------------------------------------------------ |
| bamCheckBin | check bin records of a bam file |
| bamGenome | print the genome (sequence table) of a bam file |
| bamToBigWig | convert bam to bigWig (estimate fragment length if required) |
| bamView | print contents of a bam file |
| bigWigEditChromNames | edit chromosome names of a bigWig file (i.e. replace chr1 by just 1) |
| bigWigExtract | extract regions from a bigWig file and save them as table or bigWig file |
| bigWigExtractChroms | extract a subset of the chromosomes from a bigWig file |
| bigWigGenome | print the genome (sequence table) of a bigWig file |
| bigWigHistogram | compute a histogram of the values in a bigWig file |
| bigWigNil | read bigWig and output it to a new file |
| bigWigMap | apply a function to a set of bigWig files |
| bigWigPositive | simple peak finding (i.e. every region with a value above a threshold) |
| bigWigQuantileNormalize | quantile normalize a bigWig file to a reference |
| bigWigQuery | retrieve data from a bigWig file |
| bigWigQuerySequence | retrieve sequences from a bigWig file |
| bigWigStatistics | compute summary statistics of a bigWig file |
| chromHmmTablesToBigWig | convert chromHmm output (posteriors / binariezed bams) to bigWig |
| countKmers | count kmers in a set of DNA sequences |
| drawGenomicRegions | draw random genomic regions |
| fastaExtract | extract regions from a fasta file |
| fastaUnresolvedRegions | identify regions that are not resolved (i.e. stretches of 'NNNN...') |
| gtfToBed | convert GTF files to Bed6 format |
| memeExtract | extract PWM/PPM motifs from MEME/DREME xml files |
| observedOverExpectedCpG | compute CpG scores as defined by Gardiner-Garden and Frommer (1987) |
| pwmScanSequences | scan sequences for PWM hits |
| pwmScanRegions | scan regions for multiple PWMs |
| segmentationDifferential | extract differential regions from multiple chromatin segmentations |
GRanges
Create a GRanges object with three ranges on the first chromosome:
seqnames := []string{"chr1", "chr1", "chr1"}
from := []int{100000266, 100000271, 100000383}
to := []int{100000291, 100000296, 100000408}
strand := []byte{'+', '+', '-'}
granges := NewGRanges(seqnames, from, to, strand)
fmt.Println(granges)
seqnames ranges strand
1 chr1 [100000266, 100000291) +
2 chr1 [100000271, 100000296) +
3 chr1 [100000383, 100000408) -
Add some meta data to the GRanges object:
granges.AddMeta("data", []float64{1.0, 2.0, 3.0})
seqnames ranges strand | data
1 chr1 [100000266, 100000291) + | 1.000000
2 chr1 [100000271, 100000296) + | 2.000000
3 chr1 [100000383, 100000408) - | 3.000000
Find overlaps of two GRanges objects:
rSubjects := NewGRanges(
[]string{"chr4", "chr4", "chr4", "chr4"},
[]int{100, 200, 300, 400},
[]int{150, 250, 350, 450},
[]byte{})
rQuery := NewGRanges(
[]string{"chr1", "chr4", "chr4", "chr4", "chr4", "chr4"},
[]int{100, 110, 190, 340, 390, 450},
[]int{150, 120, 220, 360, 400, 500},
[]byte{})
queryHits, subjectHits := FindOverlaps(rQuery, rSubjects)
queryHits: [1 2 3 4 5]
subjectHits: [0 1 2 3 3]
Genes
Download gene list from UCSC and export it to file:
genes := ImportGenesFromUCSC("hg19", "ensGene")
genes.WriteTable("hg19.knownGene.txt", true, false)
fmt.Println(genes)
names seqnames transcripts cds strand
1 ENST00000456328 chr1 [ 11868, 14409) [ 14409, 14409) +
2 ENST00000515242 chr1 [ 11871, 14412) [ 14412, 14412) +
3 ENST00000518655 chr1 [ 11873, 14409) [ 14409, 14409) +
4 ENST00000450305 chr1 [ 12009, 13670) [ 13670, 13670) +
5 ENST00000423562 chr1 [ 14362, 29370) [ 29370, 29370) -
... ... ... ...
204936 ENST00000420810 chrY [28695571, 28695890) [28695890, 28695890) +
204937 ENST00000456738 chrY [28732788, 28737748) [28737748, 28737748) -
204938 ENST00000435945 chrY [28740997, 28780799) [28780799, 28780799) -
204939 ENST00000435741 chrY [28772666, 28773306) [28773306, 28773306) -
204940 ENST00000431853 chrY [59001390, 59001635) [59001635, 59001635) +
Import expression data from a GTF file:
genes.ImportGTF("genesExpr_test.gtf.gz", "transcript_id", "FPKM", false)
names seqnames transcripts cds strand | expr
1 ENST00000456328 chr1 [ 11868, 14409) [ 14409, 14409) + | 0.073685
2 ENST00000515242 chr1 [ 11871, 14412) [ 14412, 14412) + | 0.000000
3 ENST00000518655 chr1 [ 11873, 14409) [ 14409, 14409) + | 0.000000
4 ENST00000450305 chr1 [ 12009, 13670) [ 13670, 13670) + | 0.000000
5 ENST00000423562 chr1 [ 14362, 29370) [ 29370, 29370) - | 10.413931
... ... ... ... | ...
204936 ENST00000420810 chrY [28695571, 28695890) [28695890, 28695890) + | 0.000000
204937 ENST00000456738 chrY [28732788, 28737748) [28737748, 28737748) - | 0.000000
204938 ENST00000435945 chrY [28740997, 28780799) [28780799, 28780799) - | 0.000000
204939 ENST00000435741 chrY [28772666, 28773306) [28773306, 28773306) - | 0.000000
204940 ENST00000431853 chrY [59001390, 59001635) [59001635, 59001635) + | 0.000000
Peaks
Import peaks from a MACS xls file:
peaks := ImportXlsPeaks("peaks_test.xls")
seqnames ranges strand | abs_summit pileup -log10(pvalue) fold_enrichment -log10(qvalue)
1 2L [ 5757, 6001) * | 5865 33.000000 19.809300 6.851880 17.722200
2 2L [ 47233, 47441) * | 47354 36.000000 19.648200 6.263150 17.566200
3 2L [ 66379, 67591) * | 66957 252.000000 350.151250 50.986050 346.525450
4 2L [ 72305, 72838) * | 72525 170.000000 208.558240 34.460930 205.734390
5 2L [ 72999, 73218) * | 73130 25.000000 12.711700 5.239670 10.700880
... ... | ... ... ... ... ...
12 2R [3646319, 3646794) * | 3646442 37.000000 23.176910 7.455710 21.063850
13 2R [3666770, 3668041) * | 3667119 215.000000 279.229060 41.551060 276.108090
14 2R [3668231, 3668441) * | 3668363 22.000000 9.943110 4.476950 7.976070
15 2R [3670063, 3670393) * | 3670180 38.000000 19.474590 5.901360 17.393440
16 2R [3670470, 3670927) * | 3670719 227.000000 305.243350 45.831180 301.974760
Track
Import ChIP-seq reads from bed files and create a track with the normalized signal:
fmt.Fprintf(os.Stderr, "Parsing reads (treatment) ...\n")
treatment1 := GRanges{}
treatment1.ImportBed6("SRR094207.bed")
treatment2 := GRanges{}
treatment2.ImportBed6("SRR094208.bed")
fmt.Fprintf(os.Stderr, "Parsing reads (control) ...\n")
control1 := GRanges{}
control1.ImportBed6("SRR094215.bed")
control2 := GRanges{}
control2.ImportBed6("SRR094216.bed")
genome := Genome{}
genome.Import("Data/hg19.genome")
d := 200 // d=200 (see *_peaks.xls)
binsize := 100 // binsize of the track
pcounts := 1 // pseudocounts
track := NormalizedTrack("H3K4me3",
[]GRanges{treatment1, treatment2}, []GRanges{control1, control2},
genome, d, binsize, pcounts, pcounts, false)
Export track to wig or bigWig:
track.WriteWiggle("track.wig", "track description")
track.WriteBigWig("track.bw", "track description")
BigWig Files
BigWig files contain data in a binary format optimized for fast random access. In addition to the raw data, bigWig files typically contain several zoom levels for which the data has been summarized. The BigWigReader class allows to query data and it automatically selects an appropriate zoom level for the g
