ChIP-seq analytics and confidence estimation by CHANCE
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CHANCE is a software tool that analyzes ChIP-seq data and estimates the confidence and strength of immunoprecipitation. It uses signal extraction scaling to decompose peaks into those pulled by the antibody versus background. It also estimates the percentage of enriched data and a normalization factor. CHANCE provides graphics to show results, allows cross-validation with other data, and identifies failed experiments. It has a graphic interface and is easy to install but is only available for certain genomes and requires MATLAB.
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Go Get Data (GGD) is a genomics data management system that provides access to processed and curated genomic data files. It allows users to create "data recipes" that define genomic data files and their metadata. These recipes are used to generate data packages that can be installed and their files accessed via environment variables. GGD also supports finding, installing, uninstalling, and listing installed data packages.
fastp: the FASTQ pre-processor
The document discusses fastp, an ultra-fast all-in-one FASTQ preprocessor. Fastp performs adapter trimming, quality trimming, base correction, polyG/polyX tail trimming, and can handle UMIs. It is very fast due to being written in C++ and multi-threaded. Fastp outputs metrics that can be integrated into MultiQC reports. The document provides examples of fastp commands and usage with GNU Parallel for processing multiple samples simultaneously.
R markdown and Rmdformats
R markdown allows connecting data, code, and text into reports, presentations, and other documents. It works with R, Python, and Bash code. The rmdformats package creates clean HTML documents from R markdown files using different template designs like "readthedown" and "docute". Templates allow formatting code and content into pages, tables of contents, and other features. Parameters control template features such as figure sizes and code folding. Resources for learning more about R markdown and rmdformats were also provided.
File searching tools
This document discusses various file searching tools. It introduces grep for searching files using regular expressions. Faster alternatives to grep like Ag, Ack, and Ripgrep are presented. The document also covers finding files using find or fd, fuzzy filtering with FZF, code searching with ctags or language servers, and summarizes to consider faster tools when possible and leverage editor plugins for code context.
Better BibTeX (BBT) for Zotero
The document discusses Better BibTeX (BBT), an add-on for the desktop version of Zotero. BBT improves on the standard BibTeX file export from Zotero by handling key formatting, duplicates, special characters, and journal abbreviations to produce cleaner BibTeX files that are suitable for use in LaTeX documents on platforms like Overleaf.
Awk primer and Bioawk
Bioawk is a tool that extends GNU awk to facilitate working with biological file formats like FASTA, FASTQ, SAM, BED, GFF, and VCF. It directly reads gzipped files and treats spanning sequences as single records. Some key functions added in Bioawk include calculating GC content, reversing/reverse complementing sequences, and working with quality values. Bioawk allows for convenient parsing, manipulation and statistical analysis of genomic data.
Terminals and Shells
This document discusses terminals and shells. It defines that a shell is a program that interprets commands from a user and executes those commands, while a terminal is a physical device for displaying output and reading input. It provides a brief history of terminals, from telexes and teletypewriters to modern terminal emulators. It also covers terminal configuration, customization, multiplexing using software like tmux and screen, and pseudoterminals. Finally, it discusses different shells, how to choose a shell, shell modes of operation, and shell configuration files like bashrc and profiles.
BioRender & Glossary/Acronym
This document discusses molecular biology concepts for computer scientists and tools for creating glossaries and displaying acronyms. It introduces BioRender, a tool for creating biological diagrams and illustrations. It then evaluates different LaTeX packages for creating glossaries and displaying acronyms, finding that the glossaries-extra package allows for creating both a glossary and acronyms. It concludes that BioRender is easy to use and has a useful icon library, and that glossaries-extra is effective for defining terms and acronyms.
Linters in R
Linters in R provides a 3 sentence summary of the document:
The document discusses the R package lintr, which checks R code for adherence to style guidelines, syntax errors, and possible semantic issues. It describes how to install lintr for use with RStudio, Emacs, and Vim and configure which checks or "linters" are applied. The document also gives examples of what lintr checks for, such as syntax, formatting, code quality, and provides information on customizing lintr using a project-specific configuration file.
BioSyntax: syntax highlighting for computational biology
bioSyntax is syntax highlighting software for computational biology. It highlights nucleotides, amino acids, and quality scores in common file formats used in bioinformatics like FASTA, FASTQ, SAM, BAM, VCF, GTF and custom formats. bioSyntax works in many text editors and terminals to help visualize and interpret genomic data. It supports common color schemes and allows customizing colors for specific nucleotides, amino acids or quality scores to highlight features of interest in sequences and alignments.
Get Good With Git
This document provides an overview and introduction to using the version control system Git. It covers basic Git concepts and operations including configuration, the three main states files can be in, committing changes, viewing history and logs, branching, merging, rebasing, tagging, and collaborating remotely. The document also discusses some internals of Git including how objects are stored and how Git and other version control systems originated.
Tech Talk: UCSC Genome Browser
The document discusses the UCSC Genome Browser, an online tool for viewing and interacting with genomic data. It allows users to view multiple data sources simultaneously for a genomic region across many organisms. The document covers basic usage, uploading temporary custom tracks, creating permanent track hubs to host data, and sharing views using saved sessions. Track hubs and sessions allow sharing genomic views and custom data without time limits.
MultiQC: summarize analysis results for multiple tools and samples in a singl...
MultiQC is a tool that aggregates bioinformatics quality control (QC) results from different tools into a single HTML report. It currently supports 73 tools and can integrate QC metrics from preprocessing, alignment, and post-alignment stages. MultiQC generates interactive plots and tables in an customizable report to allow users to compare QC metrics across multiple samples and tools in an flexible manner.
dreamRs: interactive ggplot2
Esquisse is an R package called dreamRs that provides an interactive graphical user interface for creating ggplot2 graphs. The interface allows users to build plots by selecting aesthetic mappings and geoms directly in the UI without writing any code. dreamRs can be launched from within R or installed directly from GitHub, and it offers various customization options for adjusting plot properties through the visual interface.
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Efficient querying of genomic reference databases with gget
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- 1. ChIP-seq analytics and confidence estimation by CHANCE Mehran Karimzadeh A Hoffman lab tech talk
- 2. What you can do by CHANCE ● Strength of immunoprecipitation: insufficient depths, PCR bias, batch effects ● Sequence content/quality: biases by sonication, chemical digestion & library prep ● Cross validation with ChIP-qPCR and public profiles (ENCODE)
- 3. Estimating strength of IP enrichment Signal Extraction Scaling (based on order statistics) to decompose peaks in two groups: Those pulled by antibody and background. SES estimates percentage of data enriched for biological signal and a normalization factor to properly normalize IP and input together. Based on divergence statistic of thousands of ENCODE experiments, it reports a pFDR for IP enrichment level that may identify failed experiments.
- 5. Results by CHANCE (I)
- 6. Results by CHANCE (II)
- 7. Results by CHANCE (III)
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- 9. Read density module Uses spectral analysis to identify if density of reads follows a poisson distribution according to distance from TSS