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14810 skills availablebio-hi-c-analysis-hic-differential
Reference examples tested with: cooler 0.9+, cooltools 0.6+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+, sta...
bio-hi-c-analysis-hic-data-io
Reference examples tested with: cooler 0.9+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+ Before using code patte...
bio-hi-c-analysis-contact-pairs
Reference examples tested with: cooler 0.9+, pairtools 1.1+, pandas 2.2+ Before using code patterns, verify installed ve...
bio-hi-c-analysis-compartment-analysis
Reference examples tested with: cooler 0.9+, cooltools 0.6+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+ Befo...
bio-genome-intervals-proximity-operations
Operations for finding nearby features and extending intervals using bedtools and pybedtools. bedtools closest -a peaks....
bio-genome-intervals-gtf-gff-handling
GTF and GFF3 are standard gene annotation formats. Both use 1-based coordinates. | Feature | GTF | GFF3 | |---------|---...
bio-genome-engineering-off-target-prediction
Reference examples tested with: pandas 2.2+ Before using code patterns, verify installed versions match. If versions dif...
bio-genome-assembly-scaffolding
bwa index draftassembly.fa bwa mem -5SP -t 16 draftassembly.fa hicR1.fq.gz hicR2.fq.gz | \ samtools view -@ 8 -bhS - > a...
bio-genome-assembly-metagenome-assembly
Metagenome assembly reconstructs genomes from mixed microbial communities. Long reads enable recovery of complete circul...
bio-genome-assembly-hifi-assembly
hifiasm -o outputprefix -t 32 reads.hifi.fastq.gz awk '/^S/{print ">"$2;print $3}' outputprefix.bp.pctg.gfa > assembly.f...
bio-genome-assembly-contamination-detection
checkm2 predict --input assembly.fa --output-directory checkm2output --threads 16 checkm2 predict --input genomes/ --out...
bio-format-conversion
Reference examples tested with: BioPython 1.83+, samtools 1.19+ Before using code patterns, verify installed versions ma...
bio-flow-cytometry-gating-analysis
Reference examples tested with: flowCore 2.14+ Before using code patterns, verify installed versions match. If versions ...
bio-flow-cytometry-fcs-handling
Reference examples tested with: flowCore 2.14+, scanpy 1.10+ Before using code patterns, verify installed versions match...
bio-flow-cytometry-doublet-detection
Reference examples tested with: flowCore 2.14+, ggplot2 3.5+ Before using code patterns, verify installed versions match...
bio-flow-cytometry-cytometry-qc
Reference examples tested with: flowCore 2.14+, ggplot2 3.5+ Before using code patterns, verify installed versions match...
bio-flow-cytometry-bead-normalization
Reference examples tested with: flowCore 2.14+, ggplot2 3.5+ Before using code patterns, verify installed versions match...
bio-expression-matrix-gene-id-mapping
import mygene import pandas as pd mg = mygene.MyGeneInfo() ensemblids = ['ENSG00000141510', 'ENSG00000012048', 'ENSG0000...
bio-expression-matrix-counts-ingest
import pandas as pd counts = pd.readcsv('counts.tsv', sep='\t', indexcol=0) counts = pd.readcsv('counts.csv', indexcol=0...
bio-experimental-design-power-analysis
Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficien...
bio-experimental-design-multiple-testing
Testing 20,000 genes at p < 0.05 yields 1,000 false positives by chance. Correction is essential. padj <- p.adjust(pvalu...
bio-experimental-design-batch-design
Batch effects are unavoidable. Good design makes them correctable. Never confound batch with condition - Each batch must...
bio-epitranscriptomics-modification-visualization
library(Guitar) library(TxDb.Hsapiens.UCSC.hg38.knownGene) peaks <- import('m6apeaks.bed') GuitarPlot( peaks, txdb = TxD...
bio-epitranscriptomics-m6a-peak-calling
library(exomePeak2) result <- exomePeak2( bamip = c('IPrep1.bam', 'IPrep2.bam'), baminput = c('Inputrep1.bam', 'Inputrep...