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Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760

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

Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 146df33e2e44afa2a608ac72c036e6b6b871af93

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 5e92165ac2c11608ab2db42fe2d66eabe72dbb40

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: a34f47d1e37af51e387ecdfa5c3047f106c1146b

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 146df33e2e44afa2a608ac72c036e6b6b871af93

Cross-Linking ImmunoPrecipitation ================================= `CLIP` (`cross-linking immunoprecipitation`) is a method used in molecular biology that combines UV cross-linking with immunoprecipitation in order to analyse protein interactions with RNA or to precisely locate RNA modifications (e.g. m6A). (Uhl|Houwaart|Corrado|Wright|Backofen|2017)(Ule|Jensen|Ruggiu|Mele|2003)(Sugimoto|König|Hussain|Zupan|2012)(Zhang|Darnell|2011) (Ke| Alemu| Mertens| Gantman|2015) CLIP-based techniques can be used to map RNA binding protein binding sites or RNA modification sites (Ke| Alemu| Mertens| Gantman|2015)(Ke| Pandya-Jones| Saito| Fak|2017) of interest on a genome-wide scale, thereby increasing the understanding of post-transcriptional regulatory networks. The identification of sites where RNA-binding proteins (RNABPs) interact with target RNAs opens the door to understanding the vast complexity of RNA regulation. UV cross-linking and immunoprecipitation (CLIP) is a transformative technology in which RNAs purified from _in vivo_ cross-linked RNA-protein complexes are sequenced to reveal footprints of RNABP:RNA contacts. CLIP combined with high-throughput sequencing (HITS-CLIP) is a generalizable strategy to produce transcriptome-wide maps of RNA binding with higher accuracy and resolution than standard RNA immunoprecipitation (RIP) profiling or purely computational approaches. The application of CLIP to Argonaute proteins has expanded the utility of this approach to mapping binding sites for microRNAs and other small regulatory RNAs. Finally, recent advances in data analysis take advantage of cross-link–induced mutation sites (CIMS) to refine RNA-binding maps to single-nucleotide resolution. Once IP conditions are established, HITS-CLIP takes ~8 d to prepare RNA for sequencing. Established pipelines for data analysis, including those for CIMS, take 3–4 d. Workflow -------- CLIP begins with the in-vivo cross-linking of RNA-protein complexes using ultraviolet light (UV). Upon UV exposure, covalent bonds are formed between proteins and nucleic acids that are in close proximity. (Darnell|2012) The cross-linked cells are then lysed, and the protein of interest is isolated via immunoprecipitation. In order to allow for sequence specific priming of reverse transcription, RNA adapters are ligated to the 3' ends, while radiolabeled phosphates are transferred to the 5' ends of the RNA fragments. The RNA-protein complexes are then separated from free RNA using gel electrophoresis and membrane transfer. Proteinase K digestion is then performed in order to remove protein from the RNA-protein complexes. This step leaves a peptide at the cross-link site, allowing for the identification of the cross-linked nucleotide. (König| McGlincy| Ule|2012) After ligating RNA linkers to the RNA 5' ends, cDNA is synthesized via RT-PCR. High-throughput sequencing is then used to generate reads containing distinct barcodes that identify the last cDNA nucleotide. Interaction sites can be identified by mapping the reads back to the transcriptome.

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

Path: workflows/clipseq-se.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba