BOL: Related items

Curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism

Rahul Nayak — Sun, 23 Feb 2020 02:17:30 -0600

MCBI have a curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples. To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms. The new databases are:

*16S ribosomal RNA (Bacteria and Archaea)

*18S ribosomal RNA sequences (SSU) from Fungi type and reference material

*28S ribosomal RNA sequences (LSU) from Fungi type and reference material

*Internal transcribed spacer region (ITS) from Fungi type and reference material

You can also download these from the BLAST db FTP area. See the NCBI Insights post for more detail.

Useful links

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BLAST form with rRNA/ITS databases

BLAST db download

Targeted loci

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

Julia Programming Language, a Python and R rival

Radha Agarkar — Sat, 25 Aug 2018 04:46:39 -0500

Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations.

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

QUAST-LG: Versatile genome assembly evaluation

Jit — Thu, 25 Oct 2018 10:46:55 -0500

QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.

AVAILABILITY AND IMPLEMENTATION:

http://cab.spbu.ru/software/quast-lg

Address of the bookmark: http://cab.spbu.ru/software/quast-lg/

REAPR: a universal tool for genome assembly evaluation

Jitendra Prajapati — Wed, 06 Apr 2016 18:26:31 -0500

REAPR is a tool that evaluates the accuracy of a genome assembly using mapped paired end reads, without the use of a reference genome for comparison. It can be used in any stage of an assembly pipeline to automatically break incorrect scaffolds and flag other errors in an assembly for manual inspection. It reports mis-assemblies and other warnings, and produces a new broken assembly based on the error calls.

The software requires as input an assembly in FASTA format and paired reads mapped to the assembly in a BAM file. Mapping information such as the fragment coverage and insert size distribution is analysed to locate mis-assemblies. REAPR works best using mapped read pairs from a large insert library (at least 1000bp). Additionally, if a short insert Illumina library is also available, REAPR can combine this with the large insert library in order to score each base of the assembly.

http://www.sanger.ac.uk/science/tools/reapr

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-5-r47

gEVAL: Genome Evaluation Browser

Jit — Tue, 18 Jun 2019 05:39:08 -0500

The gEVAL Browser allows the evaluation of genome assemblies through its tools and pre-computed analyses.

The strength of this browser is the ability to navigate an up to date assembly and identify problematic regions and assist in strategizing potential solutions for these issues.

This facilitates the improvement of overall assemblies to a “gold” standard for release as reference genomes

Address of the bookmark: https://geval.sanger.ac.uk/index.html

320000 viruses in mammals yet to sequenced in future!!!

Rahul Agarwal — Tue, 03 Sep 2013 08:35:30 -0500

With current biological technique improvements, finally it is now possible to look at millions of unknown viruses at genomic level and understand the mechanism. According to available data, close to 70 per cent of emerging viral diseases such as HIV/AIDS, West Nile, Ebola, SARS, and influenza, are zoonoses - infections of animals that cross into humans.

To address the challenges of describing and estimating virodiversity, a team of investigators from Center for Infection and Immunity (CII) and EcoHealth Alliance began in jungles of Bangladesh - home to the flying fox.

Reference:

http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms

http://www.bbc.co.uk/news/science-environment-23932400

The Graveley Lab

Tue, 19 Nov 2013 18:02:48 -0600

Research in the Graveley lab is primarily focused on the regulation of alternative splicing and small RNA mediated gene regulation. These are fascinating and extraordinarily important mechanisms by which genes can be regulated. Our long-term goals are to understand how these processes are regulated at a mechanistic level and to understand the logic of these processes in significant biological settings. To achieve these goals, we strive to think outside the box to creatively attack the problems being addressed using a wide variety of approaches that include biochemistry, genetics, imaging, deep sequencing, large-scale RNAi screening and bioinformatics.

Lab page @ http://graveleylab.cam.uchc.edu/Graveley/index.html

Genetic code - Amino Acid

Poonam Mahapatra — Sun, 26 Oct 2014 07:45:58 -0500

The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

The image summarise all in one.

More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

R-chie

Jit — Thu, 01 Sep 2016 11:47:24 -0500

R-chie allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information.
R4RNA is the R package powering R-chie, available for download and local use for more customized figures and scripting.

http://www.e-rna.org/r-chie/plot.cgi?eg=single

Address of the bookmark: http://www.e-rna.org/r-chie/plot.cgi?eg=single

Salzberg lab

Mon, 15 May 2017 05:14:01 -0500

We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.

https://salzberg-lab.org/