BOL: Related items

iRNAD: a computational tool for identifying D modification sites in RNA sequence

Abhimanyu Singh — Thu, 16 May 2019 00:20:07 -0500

iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification.

http://lin-group.cn/server/iRNAD/

Address of the bookmark: http://lin-group.cn/server/iRNAD/

Apollo: a sequence annotation editor

Abhimanyu Singh — Tue, 27 Aug 2019 08:08:47 -0500

The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-12-research0082

GfaViz: flexible and interactive visualization of GFA sequence graphs

Jit — Thu, 23 Jan 2020 07:33:46 -0600

GFA (Graphical Fragment Assembly) is an emerging standard format for representing sequence graphs. Although it was originally conceived as a format for sequence assembly (hence the name), and this remains its core application, it is more general, and able to represent many different types of sequence graphs, including scaffolding graphs, alignment graphs, variant graphs and splicing graphs.

Address of the bookmark: https://github.com/ggonnella/gfaviz

RNA-Bloom: a fast and memory-efficient de novo transcript sequence assembler

LEGE — Thu, 09 Jul 2020 03:13:06 -0500

RNA-Bloom is a fast and memory-efficient de novo transcript sequence assembler. It is designed for the following sequencing data types:

single-end/paired-end bulk RNA-seq (strand-specific/agnostic)
paired-end single-cell RNA-seq (strand-specific/agnostic)
nanopore RNA-seq (PCR cDNA/direct cDNA/direct RNA)

Written by Ka Ming Nip ✉️

Address of the bookmark: https://github.com/bcgsc/RNA-Bloom

UniAligner: a parameter-free framework for fast sequence alignment

Abhi — Fri, 08 Mar 2024 23:36:12 -0600

UniAligner (formerly, TandemAligner) is the first parameter-free algorithm for sequence alignment that introduces a sequence-dependent alignment scoring that automatically changes for any pair of compared sequences. Classical alignment approaches, such as the Smith-Waterman algorithm, that work well for most sequences, fail to construct biologically adequate alignments of extra-long tandem repeats (ETRs), such as human centromeres and immunoglobulin loci. This limitation was overlooked in the previous studies since the sequences of the centromeres and other ETRs across multiple genomes only became available recently.

More at https://www.nature.com/articles/s41592-023-01970-4

Address of the bookmark: https://github.com/seryrzu/unialigner

Arvados

Martin Jones — Sat, 20 Sep 2014 16:54:21 -0500

Arvados is a free and open source bioinformatics platform for genomic and biomedical data. User can Store | Organize | Compute | Share the data for free.

Address of the bookmark: https://arvados.org/

QuIN’s web server

Jit — Mon, 27 Jun 2016 10:44:16 -0500

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.

AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:https://github.com/UcarLab/QuIN/.

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809

Fancy Oneliner for Bioinformatics !!

Poonam Mahapatra — Thu, 07 Jul 2016 12:05:50 -0500

This webpage lists some of the one-liners that we frequently use in metagenomic analyses. You can click on the following links to browse through different topics. You can copy/paste the commands as they are in your terminal screen, provided you follow the same naming conventions and folder structures as we have. We are sharing these codes with the intention that if they are useful and help you in your analyses, then we will be appropriately credited as considerable effort has been put into devising them.

Address of the bookmark: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/oneliners.html

Randomness and Probability

Jit — Tue, 08 Nov 2016 07:17:32 -0600

Randomness and Probability

Randomness and probability are two differnet concepts: probaility is a measure (according to measure theory) which measures the randomness. Randomness is the object to be measured by probability. For example, probability is a mapping from randomness to the real number between 0 and 1. The similar examples are that the entropy measures the uncertanity; product of length and width measures the area of rectangle etc.

Please see “A mathematical theory of ability measure” by N. Kong ets for more examples to answer this question.

Bioistats Online course

Abhimanyu Singh — Thu, 10 Nov 2016 04:22:51 -0600

One of our primary focuses will be to develop an understanding of the various ways in which we can assign a probability to some chance event. We'll also learn the fundamental properties of probability, investigate how probability behaves, and learn how to calculate the probability of a new chance event.

This book is handy understanding basic concepts.

Address of the bookmark: https://onlinecourses.science.psu.edu/stat414/node/287