BOL: Related items

Flye: Fast and accurate de novo assembler for single molecule sequencing reads

BioJoker — Tue, 02 Apr 2019 21:54:55 -0500

Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.

Address of the bookmark: https://github.com/fenderglass/Flye

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Jit — Fri, 24 Jan 2020 04:09:15 -0600

MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

Perl One liner basics !!

Abhimanyu Singh — Sun, 24 May 2015 09:28:33 -0500

Perl has a ton of command line switches (see perldoc perlrun), but I'm just going to cover the ones you'll commonly need to debug code. The most important switch is -e, for execute (or maybe "engage" :) ). The -e switch takes a quoted string of Perl code and executes it. For example:

$ perl -e 'print "Hello, World!\n"'
Hello, World!

It's important that you use single-quotes to quote the code for -e. This usually means you can't use single-quotes within the one liner code. If you're using Windows cmd.exe or PowerShell, you must use double-quotes instead.

I'm always forgetting what Perl's predefined special variables do, and often test them at the command line with a one liner to see what they contain. For instance do you remember what $^O is?

$ perl -e 'print "$^O\n"'
linux

It's the operating system name. With that cleared up, let's see what else we can do. If you're using a relatively new Perl (5.10.0 or higher) you can use the -E switch instead of -e. This turns on some of Perl's newer features, like say, which prints a string and appends a newline to it. This saves typing and makes the code cleaner:

$ perl -E 'say "$^O"'
linux

Pretty handy! say is a nifty feature that you'll use again and again.

A Brief Bioinformatics Tutorial

Jit — Wed, 21 May 2014 12:50:09 -0500

This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.

The tutorial is divided in three main parts:

In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
The Structure part is about molecular visualization, homology modeling and structural domain prediction.
In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

Machine Learning !!!

Gudiya Pal — Fri, 01 Jul 2016 12:57:12 -0500

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

An Introduction to Applied Bioinformatics

Jit — Fri, 02 Mar 2018 04:26:38 -0600

IAB is primarily being developed by Greg Caporaso(GitHub/Twitter: @gregcaporaso) in the Caporaso Lab at Northern Arizona University. You can find information on the courses I teach on my teaching website and information on my research and lab on my lab website.

Address of the bookmark: http://readiab.org/

Bioinformatics in Africa: Part3 - Mali

BioStar — Sat, 06 Feb 2021 13:28:44 -0600

International Center for Excellence in Research (ICER):

The ICER is a research center composed of the following three programs: 1. The Malaria Research and Training Center Parasitology Group, 2. The Malaria Research and Training Center Entomology Group 3. The SEREFO Group.

The first two programs develop biomedical researches in malaria, Filariasis and Leishmaniasis. The third program develops biomedical researches in tuberculosis and HIV.

Bioinformatics was introduced recently to the ICER and is constantly growing. The ICER has one team, headed by Sidy SOUMARE, which supports the three programmes in all their needs in informatics and bioinformatics. This team can beneficiate from some computational facilities (4 blast servers, 15 other servers and around 200 terminals), but the ICER staff needs some training in order to be able to administrate these facilities.

Research Interest and Activities: The following are the present areas of research interest: 1. Functional genomics 2. Analysis of microarray data 3. Interaction between the vector and the parasite.

Bioinformatics in Africa: Part7 - Tunisia

BioStar — Sat, 06 Feb 2021 21:25:09 -0600

Institut Pasteur de Tunis (IPT):
The IPT is a research institution founded in 1883. IPT is under the supervision of the Ministry of Health and is part of the Université El Manar of Tunis (Ministry of high Education). The missions of the institute are: Public Health Laboratory activities (PHL), Research on infectious diseases, and R/D on vaccines. Research programs are mainly oriented towards local health problems such as leishmaniais, viral hepatitis, and scorpion venoms. The group of Bioinformatics and Modelling of the IPT is hosted by the Laboratoire d’Immunopathologie Vaccinologie et Génétique Moléculaire (LIVGM), and exists since the beginning of 2005. Its present research activities include: genome annotation, EST clustering and modelling of the host/parasite response to Leishmania infection. It consists of two senior scientists, two PhD students and one MSc student

Centre de Biotechnology de Sfax (CBS):
Bioinformatics activity started at CBS in 2001 with the settingup of a research and service unit of bioinformatics. This unit currently includes one senior researcher, one engineer and four Phd students. Activities include sequence annotation (service) and three research programs: ab initio prediction of short eukaryote genes, statistical modelling by Bayesian networks approach of signal transduction pathways and statistical analysis of human sequence variation data (haplotype reconstruction and linkage disequilibrium). Activities of the Bioinformatics unit could be found at the website: http://www.cbs.rnrt.tn/ and the research activity report is available under request to Bioinformatics@cbs.rnrt.tn. Although the computing facilities are good, there is still a need for trained human resources to strengthen bioinformatics capacities at CBS, particularly in structural bioinformatics.

Web site and links: http://www.cbs.rnrt.tn

Basic Structure of Snakemake Pipeline Run !

Abhi — Thu, 14 Oct 2021 07:01:38 -0500

/user/snakemake-demo$ ls

config.json data envs scripts slurm-240702.out Snakefile

data = mock data for the snakefile to use
Snakefile = name of the snakemake “formula” file
- Note: The default file that snakemake looks for in the current working directory is the Snakefile. If you would like to override that you can specify it following the -s
  - snakemake -s snakefile.py
envs = directory for storing the conda environments that the workflow will use.
scripts = directory for storing python scripts called by the snakemake formula.
config.json = json format file with extra parameters for our snakemake file to use.
cluster.json = json format file with specification for running on the HPC
samples.txt = file we will use later relating to the config.json file.

Run the snakemake file as a dry run (the example workflow shown above).

This will build a DAG of the jobs to be run without actually executing them.
snakemake --dry-run

User can execute rules of interest.

snakemake --dry-run all VS. snakemake --dry-run call VS. snakemake --dry-run bwa

Run the snakemake file in order to produce an image of the DAG of jobs to be run.

snakemake --dag | dot -Tsvg > dag.svg OR snakemake --dag | dot -Tsvg > dag.svg

Run the snakemake (this time not as a dry run)

snakemake --use-conda

Prepare for Coding Interview !

Abhi — Tue, 11 Jan 2022 06:14:41 -0600

This is a comprehensive guide to prepare for your next coding interview. It's great for recent graduates and has questions and practice materials structured from traditional big tech interview formats.

While it does not include the latest developments in programming since 2019, it nails the core fundamentals in a very comprehensive and accessible way!

Credits to Kaiyu Zhang, with additional material in the appendix sourced from Reddit.

People say that interviews at Google will cover as much ground as possible. As a new college graduate, the ground that I must capture are the following. Part of the list is borrowed from a reddit post: https://www. reddit.com/r/cscareerquestions/comments/206ajq/my_onsite_interview_experience_at_google/ #bottom-comments.

1. Data structures

2. Trees and Graph algorithms

3. Dynamic Programming

4. Recursive algorithms

5. Scheduling algorithms (Greedy)

6. Caching 1

7. Sorting

8. Files

9. Computability

10. Bitwise operators

11. System design