<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/10741?offset=860 https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data Wed, 06 Sep 2023 00:14:28 -0500 https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data <![CDATA[PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data]]> Phylogenomic Analysis Pipeline for Herbarium Specimens

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

  • Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/44873/bakrep-denglish-blend-of-bakterien-repository-simplifies-access-to-this-data Wed, 13 Aug 2025 02:31:28 -0500 https://bioinformaticsonline.com/bookmarks/view/44873/bakrep-denglish-blend-of-bakterien-repository-simplifies-access-to-this-data <![CDATA[BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data]]> 2,438,386 bacterial genomes at your fingertips consistently processed & characterized, enriched with metadata, accessible via a flexible search engine.

BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data. It integrates enriched genomic information with metadata accessible via a flexible search-engine.

Key features

  • Assembly statistics: ensure data quality with genome-based key metrics
  • Taxonomic classification: robust, purely genome-based classifications (GTDB)
  • MLST: subtyping for deeper insights into genetic variation
  • Annotation: comprehensive & taxonomy-independent (Bakta)
  • Metadata: full original submission records
 

Address of the bookmark: https://bakrep.computational.bio/

]]> Neel https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival Sat, 25 Aug 2018 04:46:39 -0500 https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival <![CDATA[Julia Programming Language, a Python and R rival]]> 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.

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/38634/eyechrom-visualizing-chromosome-count-data-from-plants Tue, 08 Jan 2019 10:20:54 -0600 https://bioinformaticsonline.com/bookmarks/view/38634/eyechrom-visualizing-chromosome-count-data-from-plants <![CDATA[EyeChrom: Visualizing Chromosome Count Data From Plants]]> It's goal is to show chromosmal data per genus. Select the genus, and the plot will show the records found for it in the Chromosome Counts Database. note: Report an issue via Gihub: github.com/roszenil/CCDBcurator and github.com/RodrigoRivero/EyeChrom

https://bsapubs.onlinelibrary.wiley.com/doi/pdf/10.1002/aps3.1207

Address of the bookmark: http://eyechrom.com:3838/EyeChrom/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41328/deephic-a-generative-adversarial-network-for-enhancing-hi-c-data-resolution Tue, 03 Mar 2020 01:12:47 -0600 https://bioinformaticsonline.com/bookmarks/view/41328/deephic-a-generative-adversarial-network-for-enhancing-hi-c-data-resolution <![CDATA[DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution]]> DeepHiC is a GAN-based model for enhancing Hi-C data resolution. We developed this server for helping researchers to enhance their own low-resolution data by a few steps of clicks. Ab initio training could be performed according to our published code. We provided trained models for various depth of low-coverage sequencing Hi-C data. The depth of input data is estimated by its distribution comparing with those of the downsampled Hi-C data we used in training

Address of the bookmark: http://sysomics.com/deephic

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43374/reference-sequence-resource Wed, 15 Sep 2021 21:15:22 -0500 https://bioinformaticsonline.com/bookmarks/view/43374/reference-sequence-resource <![CDATA[Reference Sequence Resource!]]> 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/

]]> LEGE https://bioinformaticsonline.com/opportunity/view/17515/ngs-online-training Sat, 27 Sep 2014 07:42:29 -0500 <![CDATA[NGS Online Training]]> ArrayGen Technologies announces to provide online NGS training through out the globe. Now analyze your own NGS datasets from anywhere.For more information contact us at training@arraygen.com

Please visit our site at www.arraygen.com

]]> https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books Fri, 30 May 2014 04:48:04 -0500 https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books <![CDATA[Next generation sequencing(NGS) books]]> Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

]]> Abhimanyu Singh https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects Tue, 04 Aug 2015 05:14:07 -0500 https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects <![CDATA[Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?]]> Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?

]]> Strand https://bioinformaticsonline.com/opportunity/view/17966/internship-program-for-bioinformatics-biotechnology-professionals-no-of-vacancy-2 Wed, 08 Oct 2014 01:10:08 -0500 <![CDATA[Internship Program for Bioinformatics / Biotechnology Professionals (No. Of Vacancy: 2)]]> ArrayGen is offering an Internship Program for Post graduate Bioinformatics / Biotechnology students and professionals. ArrayGen Technologies provide an excellent opportunity to gain research experience and explore if a scientific career is right for you. Currently we offer positions to outstanding students interested in Next Generation Sequencing (NGS) data analysis. Applications are accepted throughout the year. Accepted students will be listed on web with their schedules. Accepted students can attend our future workshops and trainings freely at the specified venue.

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