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BaRC Best practices
These are "how-to's" detailing the methods that BaRC uses and finds to work effectively. Email BaRC if you have any questions about how or why to perform what is described on these pages.
Frequently Asked Questions (of particular interest to Whitehead scientists) are on our FAQs site.
General short-read sequencing
- Downloading and processing NCBI SRA files
- Quality control and preprocessing of short reads (fastq files)
- Mapping short reads
- SAM/BAM summarizing and processing
- SAM/BAM quality control
- Creating genome feature heatmaps from sequencing experiments
- Creating an analysis pipeline of compressed files
- Experimental design of short read sequencing experiments
Specific types of short-read sequencing experiments
- Using RNA-Seq to quantify gene levels and assay for differential expression
- Using RNA-Seq to quantify gene levels and assay for differential expression including transposable elements
- Using RNA-Seq to assemble or annotate transcripts
- Using ChIP-Seq to identify and/or quantify bound regions (peaks)
- Using ATAC-Seq to identify open chromatin
- Using Hi-C experiments to characterize genome-wide chromatin structure
- Using HiChIP experiments to characterize genome-wide chromatin contacts between regulatory elements
- Using RRBS experiments to characterize genomic DNA methylation
- Integrating expression and immunoprecipitation experiments
- Using CUT&Tag to identify DNA bound regions and chromatin marks
Single-cell genomics
Variant calling and analysis
- Using Samtools' mpileup/bcftools to call variants from short-read sequencing
- Using GATK to call variants from short-read sequencing
- Calling variants from RNA-seq data
- Manipulating VCF files
- Interpreting VCF files
Genome coordinates and genomics
- Creating genome coordinate files (bed, wig, etc) for genome browsers
- Linking genome regions to genome annotation(s)
- Extracting genome subsequences
- Identifying homologous genes/proteins
Microarrays
- Normalizing and preprocessing microarrays
- Identifying differentially expressed genes from microarrays
- Normalizing multiple public microarray datasets
Enrichment analysis
- Identifying all and/or enriched transcription factor binding sites
- Identifying enriched GO or other annotation terms in a set of genes
Statistics
- Performing and reporting statistical tests
- Calculating variation (SD, SE) for a ratio
- Performing ANOVA in R
Structural biology
- Predicting protein structures from sequence using AlphaFold
- Predicting the structures of protein complexes using AlphaFold multimer
Other topics
- Using ngsplot to make stacked heatmaps and profiles of genes or genomic regions (like ChIP-seq peaks)
- Producing a multiple sequence alignment of proteins, transcripts, or genome regions
- Mass spectrometry data analysis
- NanoString data analysis
- Pooled CRISPR screen analysis
- Searching for patterns or motifs in a DNA or protein sequence
- Clustering a matrix and creating a heatmap
- ChIP-Seq analysis bake-off results
- Submitting a sequencing dataset to GEO
Note:
See TracWiki
for help on using the wiki.