Changes between Version 16 and Version 17 of SOPs/atac_Seq

Timestamp:

08/14/20 13:35:54 (5 years ago)

Author:

gbell

Comment:

--

SOPs/atac_Seq

@@ -88 +88 @@
     * MACS' author, T.Liu, recommends using -f BAMPE if PE reads are used [[https://github.com/taoliu/MACS/issues/331]], using BAMPE option asks MACS to pileup and calculate the extension size -  works for finding accessible regions within cut sites.  The additional parameters can also be used to look only at the //exact// cut sites by Tn5 instead of the open/accessible regions [[https://github.com/taoliu/MACS/issues/145]], if so, -f BAMPE may not be suitable.
 
-  * [[https://github.com/jsh58/Genrich | Genrich]] is another piece of software for peak-calling. It has the advantage of running all of the post-alignment steps through peak-calling with one command. It can take multiple replicates. Detailed information can be found in [[https://informatics.fas.harvard.edu/atac-seq-guidelines.html|Harvard ATAC-seq Guidelines]] 
+[[https://github.com/jsh58/Genrich | Genrich]] is another piece of software for peak-calling. It has the advantages of (a) running all of the post-alignment steps through peak-calling with one command, and (b) can process multiple replicates. Detailed information can be found in [[https://informatics.fas.harvard.edu/atac-seq-guidelines.html|Harvard ATAC-seq Guidelines]] 
 
 {{{
@@ -108 +108 @@
 }}}
 
+[[https://github.com/LiuLabUB/HMMRATAC | HMMRATAC]] is another piece of software for ATAC-seq peak-calling.
 
 Calculate the FRiP score (with /nfs/BaRC_Public/BaRC_code/Python/calculate_FRiP_score/calculate_FRiP_score.py).  The FRiP (Fraction of reads in peaks) score describes the fraction of all mapped reads that fall into the called peak regions.  The higher the score, the better, preferably over 0.3, according to [https://www.encodeproject.org/atac-seq/#standards ENCODE].
@@ -118 +119 @@
 
 
-  * [[https://github.com/LiuLabUB/HMMRATAC | HMMRATAC]] is another piece of software for ATAC-seq peak-calling.
-
-=== Tips/Recommendations for ATAC-Seq ===
-//Based on presentation by H.Liu at BioC 2020 presentation //
+=== Other recommendations for ATAC-Seq ===
+//Based on presentation by H. Liu at BioC 2020 presentation //
   * Post-alignment filtering: remove mito/ChrM reads, remove duplicates, remove mapping artifacts: < 38bp and fragments > 2kb, and discordant reads
   * Unless you have high coverage, use all reads for downstream analysis and not remove mono-, di-, tri-, etc. nucleosome fragments or reads.  Otherwise, you may miss many open chromatin regions during peak calling.
@@ -128 +127 @@
   * For transcription factor footprinting, at least ~200M reads are recommended.
   * Tn5 produces 5’ overhangs of 9 bases long: pos. strand + 4 and neg strand -5 (shiftGAlignmentsList and shiftReads function) splitGcAlignmentsByCut: creates different bins of reads: NFR, mono, di, etc. Shifted reads that do not fit into any of the bins should be discarded.
-  * Use housekeeping genes to check QC: signal enrichment is expected in the regulatory regions of housekeeping genes in good ATACSeq experiments. Use IGVSnapshot function with geneNames param. splitGAlignmentsByCut: creates different bins of reads: NFR, mono, di, etc. Shifted reads that do not fit into any of the bins should be discarded.
-
+  * Use housekeeping genes to check QC: signal enrichment is expected in the regulatory regions of housekeeping genes in good ATAC-seq experiments. Use IGVSnapshot function with geneNames param. splitGAlignmentsByCut: creates different bins of reads: NFR, mono, di, etc. Shifted reads that do not fit into any of the bins should be discarded.
 
 === Further reading ===