BOL: Related items

Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences

LEGE — Sun, 31 Aug 2025 06:30:16 -0500

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

Address of the bookmark: https://github.com/MGXlab/Jaeger

Mobyle: a new full web bioinformatics framework

Jit — Sun, 07 Jan 2018 19:33:45 -0600

Mobyle, to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/25/22/3005/179064

KOBAS: a web server for gene/protein functional annotation and functional gene set enrichment

Jit — Fri, 19 Oct 2018 09:36:11 -0500

KOBAS 3.0 is a web server for gene/protein functional annotation (Annotate module) and functional gene set enrichment(Enrichment module). For Annotate module, it accepts gene list as input, including IDs or sequences, and generates annotations for each gene based on multiple databases about pathways, diseases, and Gene Ontology. For Enrichment module, it can accept either gene list or gene expression data as input, and generates enriched gene sets, corresponding name, p-value or a probability of enrichment and enrichment score based on results of multiple methods.

Address of the bookmark: http://kobas.cbi.pku.edu.cn/

Dash: a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Jit — Tue, 05 Nov 2019 06:39:48 -0600

Dash is a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Dash Bio is a suite of bioinformatics components that make it simpler to analyze and visualize bioinformatics data and interact with them in a Dash application.

The source can be found on GitHub at plotly/dash-bio.

These docs are using Dash Bio version 0.1.4.

Address of the bookmark: https://dash.plot.ly/dash-bio

PPAI: a web server for predicting protein-aptamer interactions

Jit — Fri, 12 Jun 2020 07:26:23 -0500

PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI

Address of the bookmark: http://39.96.85.9/PPAI/

Web Apollo: a web-based genomic annotation editing platform

Jit — Fri, 28 Jul 2017 04:48:17 -0500

Web Apollo is the first instantaneous, collaborative genomic annotation editor available on the web. One of the natural consequences following from current advances in sequencing technology is that there are more and more researchers sequencing new genomes. These researchers require tools to describe the functional features of their newly sequenced genomes. With Web Apollo researchers can use any of the common browsers (for example, Chrome or Firefox) to jointly analyze and precisely describe the features of a genome in real time, whether they are in the same room or working from opposite sides of the world.

Address of the bookmark: http://genomearchitect.github.io/

Frontend: Perl Web framework documentation - Andrej Sali Lab

Jit — Mon, 08 Jan 2018 22:32:03 -0600

The frontend is a set of Perl classes that displays the web interface, allowing a user to upload their input files, start a job, display a list of all jobs in the system, and get back job results. The main saliwebfrontend class must be subclassed for each web service. This class is then used to display the web pages using a set of CGI scripts that are set up automatically by the build system.

Address of the bookmark: https://saliweb.readthedocs.io/en/latest/frontend.html

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Rahul Nayak — Tue, 05 Jun 2018 09:57:11 -0500

PERGA - Paired End Reads Guided Assembler PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate. The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.

Address of the bookmark: https://github.com/hitbio/PERGA

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/