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TgIF (trans-gene insertion finder) ============================================== The TgIF algorithm returns a list of probable insertion sites in a target organism. It requires the user to provided a FASTQ file of ONT (Oxford Nanopore Technologies) reads (-f), the reference FASTA of the trans-gene (Tg) vector containing the insertion sequence (-i), and the reference FASTA of the target organism (-r). The algorithm is tailored for ONT reads from a modified nCATS[1] (nanopore Cas9-targeted sequencing) enriched library, however the algorithm will also produce informative results from a FASTQ derived from WGS (shotgun) sequencing libraries. The modified nCATS method is described here, and a brief overview can be found below. The basic workflow of TgIF is alignment (using minimap2[2]) of reads (-f) to a combined reference of the Tg vector (containing the desired insertion sequence) and target organism (ie. -i and -r are concatenated), and then searching for valleys (or gaps) in the resulting pileup of reads that map to both references at MAPQ>=30. A starting position (ps) of a valley is where the depth (d) at dp=0 and dp-1>0, an ending position (pe) of a valley is where the depth at dp=0 and dp+1>0, and a potential insertion scar is the gap between and including ps and pe. Primary Output files: - insertions_all.tsv, all probable insertion sites identified from the input fastq data - insertions_filtered.tgif, filtered sites that are most probable based on logic (4) above - reportsummary.md, summary of alignment metrics and insertion sites found Secondary Output files: - insertion_site_plots.tar, package of probable insertion site pileup plots - alignment_files.tar.gz, contains bam/bai for visualizing aligned reads to reference genome and vector sequence - primer3.tar, contains F/R primers for each filtered insertion site designed by primer3 Documents ============================================== - github Page: https://github.com/jhuapl-bio/TgIF/tree/main References ============================================== - Gilpatrick, T. et al. Targeted nanopore sequencing with Cas9-guided adapter ligation. Nature Biotechnology 38, 433–438 (2020). - Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100. doi:10.1093/bioinformatics/bty191 - O. Tange (2011): GNU Parallel - The Command-Line Power Tool, ;login: The USENIX Magazine, February 2011:42-47. - Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and 1000 Genome Project Data Processing Subgroup, The Sequence alignment/map (SAM) format and SAMtools, Bioinformatics (2009) 25(16) 2078-9 [19505943] - R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. - H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. - Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M and Rozen SG. Primer3--new capabilities and interfaces. Nucleic Acids Res. 2012 Aug 1;40(15):e115.

https://github.com/datirium/workflows.git

Path: workflows/tgif.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/module-1.cwl

Branch/Commit ID: 5bf88423593441e4bf6b432111160446cd8dcf13

ChIP-seq 02 trimming - reads: SE

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ChIP-seq_pipeline/02-trim-se.cwl

Branch/Commit ID: a502ff01b0857f8067aa541effc46a4c8b10d90f

The main workflow that: produces two reproducible peaks via IDR given two eCLIP samples (1 input, 1 IP each). runs the 'rescue ratio' statistic runs the 'consistency ratio' statistic

https://github.com/YeoLab/merge_peaks.git

Path: cwl/wf_full_IDR_pipeline_1input.cwl

Branch/Commit ID: 55f4f4f9c10a09ce03c5c531dd176e6080118977

https://github.com/ncbi/pgap.git

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: e0fb04a0d8bc648183c6b71d099ce7aea3c3b3ff

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: ddd748516b25256a461ea9277303406fa2759b00

What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.

https://github.com/datirium/workflows.git

Path: workflows/rgt-thor.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: a670f323e77e02d9b77be9a13d73d5276dd3676c

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines2-wf.cwl

Branch/Commit ID: a858bb4db58ef2df17b4856294ad7904643c5c6e

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/alignment_novoalign.cwl

Branch/Commit ID: 91e222adeeee0bd567a5bf2385400610fad0d3a9