BOL: LEGE's bookmarks

Genome Context Viewer (GCV)

LEGE — Sun, 21 May 2023 19:33:43 -0500

The Genome Context Viewer (GCV) is a web-app that visualizes genomic context data provided by third party services. Specifically, it uses functional annotations as a unit of search and comparison. By adopting a common set of annotations, data-store operators can deploy federated instances of GCV, allowing users to compare genomes from different providers in a single interface.

Address of the bookmark: https://github.com/legumeinfo/gcv

Tools for id conversion

LEGE — Sat, 20 May 2023 21:53:32 -0500

g:Convert enables to convert between various gene, protein, microarray probe and numerous other types of namespaces. We provide at least 40 types of IDs for more than 60 species. The 98 different namespaces supported for human include Ensembl, Refseq, Illumina, Entrezgene and Uniprot identifiers. All namespaces are obtained through matching them via Ensembl gene identifiers as a reference.

Address of the bookmark: https://biit.cs.ut.ee/gprofiler/convert

Proksee: in-depth characterization and visualization of bacterial genomes

LEGE — Tue, 09 May 2023 19:38:52 -0500

Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.

Address of the bookmark: https://proksee.ca/

Carrot2 clustering engine

LEGE — Fri, 07 Apr 2023 13:11:24 -0500

This is the demo application of the Carrot² clustering engine. It uses Carrot²'s algorithms to organize search results into thematic folders.

User interfaces

Web Search Clustering organizes search results from public search engines into clusters; offers treemap- and pie-chart visualizations of the clusters.
Clustering Workbench clusters content from local files in JSON or Excel format, Solr or Elasticsearch; allows tuning of clustering parameters and exporting results as Excel or JSON.

Search engines

Web: web search results provided by etools.ch. Extensive use may require special arrangements with the owner of the etools.ch service.
PubMed: abstracts of medical papers from the PubMed database provided by NCBI.
Local file: content read from a local file in Carrot2 XML, JSON, CSV or Excel format.
Solr: queries an Apache Solr instance.
Elasticsearch: queries an Elasticsearch instance.

Clustering algorithms

Lingo: creates well-described flat clusters. Does not scale beyond a few thousand search results. Available as part of the open source Carrot² framework.
STC: the classic search results clustering algorithm. Produces flat cluster with adequate description, very fast. Available as part of the open source Carrot² framework
k-means: base line clustering algorithm, produces bag-of-words style cluster descriptions. Available as part of the open source Carrot² framework

Address of the bookmark: https://search.carrot2.org/#/search/web

Research Rabbit

LEGE — Fri, 07 Apr 2023 12:01:33 -0500

You’re determined students, passionate teachers, and inspired creators of everything we know! Yet, all the researchers we spoke with over the past year uncovered the same reality:

Despite the degrees you’ve earned, the effort and passion you put in every day, the sacrifices you make – academia burdens you with tons of stressors. Financial, psychological, physical, and more.

We’re an unconventional team with backgrounds in various fields – both inside and outside of academia. And through our lens, it’s clear that academia deeply undervalues and underserves its very own community!

It’s time to reimagine research.

Address of the bookmark: https://researchrabbitapp.com/

Getting Started with Nextflow

LEGE — Sat, 18 Sep 2021 01:28:41 -0500

Introduction to Bioinformatics workflows with Nextflow and nf-core

Getting Started with Nextflow

Objectives Understand

What a workflow management system is.

Understand the benefits of using a workflow management system.

Explain the benefits of using Nextflow as part of your bioinformatics workflow.

Explain the components of a Nextflow script.

Run a Nextflow script.

Introduction to Bioinformatics workflows with Nextflow and nf-core

Getting Started with Nextflow

Address of the bookmark: https://carpentries-incubator.github.io/workflows-nextflow/aio/index.html

LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets

LEGE — Fri, 17 Sep 2021 01:57:02 -0500

The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

HISAT2 Index Files Download !

LEGE — Wed, 15 Sep 2021 22:17:49 -0500

Resource for downloading all the HISAT2 related files

Please cite:

Kim, D., Paggi, J.M., Park, C. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019). https://doi.org/10.1038/s41587-019-0201-4

Address of the bookmark: http://daehwankimlab.github.io/hisat2/download/#h-sapiens

Reference Sequence Resource!

LEGE — Wed, 15 Sep 2021 21:15:22 -0500

The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. T

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0