Here are links to all of this year's posts (excluding seminar/webinar announcements), with the most visited posts in bold italic. As always, you can follow me on Twitter for more frequent updates. Happy new year!
New Year's Resolution: Learn How to Code
Annotating limma Results with Gene Names for Affy Microarrays
Your Publications (with PMCID) as a PubMed Query
Pathway Analysis for High-Throughput Genomics Studies
find | xargs ... Like a Boss
Redesign by Subtraction
Video Tip: Convert Gene IDs with Biomart
RNA-Seq Methods & March Twitter Roundup
Awk Command to Count Total, Unique, and the Most Abundant Read in a FASTQ file
Video Tip: Use Ensembl BioMart to Quickly Get Ortholog Information
Stepping Outside My Open-Source Comfort Zone: A First Look at Golden Helix SVS
How to Stay Current in Bioinformatics/Genomics
The HaploREG Database for Functional Annotation of SNPs
Identifying Pathogens in Sequencing Data
Browsing dbGAP Results
Fix Overplotting with Colored Contour Lines
Plotting the Frequency of Twitter Hashtag Usage Over Time with R and ggplot2
Cscan: Finding Gene Expression Regulators with ENCODE ChIP-Seq Data
More on Exploring Correlations in R
DESeq vs edgeR Comparison
Learn R and Python, and Have Fun Doing It
STAR: ultrafast universal RNA-seq aligner
RegulomeDB: Identify DNA Features and Regulatory Elements in Non-Coding Regions
Copy Text to the Local Clipboard from a Remote SSH Session
Differential Isoform Expression With RNA-Seq: Are We Really There Yet?
Showing posts with label Annotation. Show all posts
Showing posts with label Annotation. Show all posts
RegulomeDB: Identify DNA Features and Regulatory Elements in Non-Coding Regions
Many papers have noted the challenges associated with assigning function to non-coding genetic variation, and since the majority of GWAS hits for common traits are non-coding, resources for providing some mechanism for these associations are desperately needed.
Boyle and colleagues have constructed a database called RegulomeDB to provide functional assignments to variants using data from manual curation, CHiP-seq data, chromatin state information, eQTLs across multiple cell lines, and some computational predictions generated from DNase footprinting and transcription factor binding motifs.
RegulomeDB implements a tiered category system (1-6) where category 1 has an eQTL association in addition to other ENCODE sources of data, 2 -5 have some ENCODE data only with no eQTL associations, and category 6 has evidence of a binding motif change only. As you might imagine, the annotation density increases as you increase category numbers.
Their simple, but impressive interface will accept RS numbers, or whole BED, GFF, or VCF files for annotation. The resulting output (example above) is downloadable, providing both specifics of the annotation (such as the transcription factor binding to the area) and the functional score for the variant.
http://regulome.stanford.edu/
Boyle and colleagues have constructed a database called RegulomeDB to provide functional assignments to variants using data from manual curation, CHiP-seq data, chromatin state information, eQTLs across multiple cell lines, and some computational predictions generated from DNase footprinting and transcription factor binding motifs.
RegulomeDB implements a tiered category system (1-6) where category 1 has an eQTL association in addition to other ENCODE sources of data, 2 -5 have some ENCODE data only with no eQTL associations, and category 6 has evidence of a binding motif change only. As you might imagine, the annotation density increases as you increase category numbers.
Their simple, but impressive interface will accept RS numbers, or whole BED, GFF, or VCF files for annotation. The resulting output (example above) is downloadable, providing both specifics of the annotation (such as the transcription factor binding to the area) and the functional score for the variant.
http://regulome.stanford.edu/
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