BOL: Related items

Modern Statistics with R

LEGE — Thu, 22 Aug 2024 04:44:06 -0500

This is the online version of the second edition of Modern Statistics with R. It is free to use, and always will be. Printed copies are available from CRC Press.

Live online courses on statistics with R based on this book, led by the author, are offered regularly; see this page for more information and dates.

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you:

Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.
Exploratory data analysis - using visualisations and multivariate techniques to explore datasets.
Statistical inference - modern methods for testing hypotheses and computing confidence intervals.
Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting.
Simulation - using simulation techniques for sample size computations and evaluations of statistical methods.
Ethics in statistics - ethical issues and good statistical practice.
R programming - writing code that is fast, readable, and (hopefully!) free from bugs.

The book includes plenty of examples and more than 200 exercises with worked solutions. The datasets used for the examples and the exercises can be downloaded here.

Address of the bookmark: https://www.modernstatisticswithr.com/

Understanding your reads and mapping !

Neel — Wed, 29 Jan 2020 06:29:55 -0600

One of the best tutorial for beginners ...

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Understanding kmer !

BioStar — Wed, 18 Aug 2021 04:27:51 -0500

What is a k-mer anyway? A k-mer is just a sequence of k characters in a string (or nucleotides in a DNA sequence). Now, it is important to remember that to get all k-mers from a sequence you need to get the first k characters, then move just a single character for the start of the next k-mer and so on. Effectively, this will create sequences that overlap in k-1 positions.

Address of the bookmark: https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/

Snakemake Tutorials !

Rahul Nayak — Mon, 09 May 2022 05:20:41 -0500

A lesson introducing the Snakemake workflow system for bioinformatics analysis.

Prerequisites

This is an intermediate lesson and assumes learners have already done some bioinformatics:

Familiarity with the BASH command shell, including concepts like pipes, variables and loops.

Knowledge of bioinformatics fundamentals like the FASTQ file format and transcriptome sequencing, in order to understand the example workflow.

No previous knowledge of Snakemake or workflow systems is required.

https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/index.html

Address of the bookmark: https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/aio/index.html

Check Linux server configuration !!

Rahul Nayak — Tue, 06 May 2014 01:10:57 -0500

Bioinformatician uses servers for computational analysis. Sometime we need to check the server details before running our programs or tools. Here I am showing some basic commands using them you can gather the system/server information.

To check what version of Operating System is installed on the server you can use the following commands:-
=================================================================
1.cat /etc/issue
[root@localhost ~]# cat /etc/issue
Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Kernel \r on an \m

2.cat /etc/redhat-release
[root@localhost ~]# cat /etc/redhat-release
Red Hat Enterprise Linux Server release 5.5 (Tikanga)

3.lsb_release -a
[root@localhost ~]# lsb_release -a
LSB Version:    :core-3.1-ia32:core-3.1-noarch:graphics-3.1-ia32:graphics-3.1-noarch
Distributor ID: RedHatEnterpriseServer
Description:    Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Release:        5.5
Codename:       Tikanga

To check whether the operating system is 32 or 64bit:-
================================
# uname -i
[root@localhost ~]# uname -i
i386
(i386 represents that server is having 32bit operating system)

[root@localhost ~]# uname -i
x86_64
(x86_64 represents that server is having 64bit operating system)

To see the processor/CPU information:-
=============================
# cat /proc/cpuinfo
[root@localhost ~] cat /proc/cpuinfo
processor       : 0
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) CPU            5130 @ 2.00GHz
stepping        : 6
cpu MHz         : 1995.087
cache size      : 4096 KB
physical id     : 0
siblings        : 2
core id         : 0
cpu cores       : 2
apicid          : 0
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm constant_tsc pni monitor ds_cpl vmx tm2 ssse3 cx16 xtpr lahf_lm
bogomips        : 3990.17
(Here processor number 0 indicates that the system is having one process(processor number starts with zero))

To check memory information:-
===========================
# free -m
[root@localhost ~]# free -m
             total       used       free     shared    buffers     cached
Mem:          5066       3513       1552          0        612       2319
-/+ buffers/cache:        582       4484
Swap:         1983          0       1983

# cat /proc/meminfo
[root@localhost ~]# cat /proc/meminfo
MemTotal:      5187752 kB
MemFree:       1639300 kB
Buffers:        627024 kB
Cached:        2374944 kB
SwapCached:          0 kB
Active:        2458788 kB
Inactive:       920964 kB
HighTotal:     4325164 kB
HighFree:      1561936 kB
LowTotal:       862588 kB
LowFree:         77364 kB
SwapTotal:     2031608 kB
SwapFree:      2031608 kB
Dirty:             704 kB
Writeback:           0 kB
AnonPages:      377892 kB
Mapped:          35328 kB
Slab:           153036 kB
PageTables:       6316 kB
NFS_Unstable:        0 kB
Bounce:              0 kB
CommitLimit:   4625484 kB
Committed_AS:   977132 kB
VmallocTotal:   116728 kB
VmallocUsed:      4492 kB
VmallocChunk:   112124 kB
HugePages_Total:     0
HugePages_Free:      0
HugePages_Rsvd:      0
Hugepagesize:     2048 kB

To check the model and serial name of the server:-
=======================================
[root@localhost ~]# dmidecode | egrep -i "product name|Serial number"
Product Name: PowerEdge R710
Serial Number: AB8CDE1


To check the host name:-
=====================
[root@localhost ~]# uname -n
localhost

[root@localhost ~]# hostname
localhost

To check the kernel version:-
========================
[root@localhost ~]# uname -r
2.6.18-238.9.1.el5PAE

Bioinformatician's summer vacation !!!

Jit — Wed, 03 Sep 2014 13:11:50 -0500

Yes, the bioinformatician do spend their summer vacation like this. They spend more time on cheking the JOBS running on various servers.

SLURM

Jit — Wed, 04 May 2016 05:13:21 -0500

SLURM workload manager software, a free open-source workload manager designed specifically to satisfy the demanding needs of high performance computing.

This page is a HOWTO guide for setting up a SLURM installation, currently focused on a CentOS 7 Linux OS. Please send feedback to Ole.H.Nielsen /at/ fysik.dtu.dk.

See the SLURM homepage (also https://computing.llnl.gov/linux/slurm/).

Address of the bookmark: https://wiki.fysik.dtu.dk/niflheim/SLURM

Binding Site Prediction in Protein !

Poonam Mahapatra — Wed, 25 Apr 2018 04:35:57 -0500

The interaction between proteins and other molecules is fundamental to all biological functions. In this section we include tools that can assist in prediction of interaction sites on protein surface and tools for predicting the structure of the intermolecular complex formed between two or more molecules (docking).

Pockets Identification

CASTp

Automatic Identification of pockets and cavities in proteins structure, and quantitation of their volumes using Delaunay triangulation. Available also as PyMOL plugin

Pocket-Finder

Automatic identification of pockets and cavities in proteins structure, and quantitation of their volumes.

PocketPicker

Grid-based technique for the analysis of protein pockets. PocketPicker available as a plugin for PyMOL

Binding Site Prediction

ConSurf

Identification of functional regions in proteins by surface-mapping of phylogenetic information

CRESCENDO

Identification protein interaction sites. It uses sequence conservation patterns in homologous proteins to distinguish between residues that are conserved due to structural restraints from those due to functional restraints.

Ligand Binding Sites

3DLigandSite

The server utilizes protein-structure prediction to provide structural models of the binding site. Ligands bound to structures are superimposed onto the model and use to predict the binding site.

FINDSITE

A threading-based method for ligand-binding site prediction and functional annotation based on binding-site similarity across superimposed groups of threading templates.

LIGSITE^csc

Prediction of binding site by pocket identification using the Connolly surface and degree of conservation

metaPocketA meta server for ligand-binding site prediction. metaPocket use LIGSITE^csc, PASS, Q-SiteFinder and SURFNET

Bioinformatics tools to explore SSRs in genomes !

BioStar — Tue, 07 Mar 2023 13:06:15 -0600

There are several bioinformatics tools that can be used to explore Simple Sequence Repeats (SSRs), which are also known as microsatellites. Here are a few examples:

MISA: MISA (MIcroSAtellite) is a web-based tool that can identify SSRs in DNA sequences. It can be used to analyze nucleotide sequences from various organisms and can identify perfect, compound, and imperfect SSRs.
SSR Locator: SSR Locator is a web-based tool that identifies SSRs in both DNA and RNA sequences. It can identify perfect, compound, and imperfect SSRs, and can also filter out low complexity regions.
SciRoKo: SciRoKo is a software tool that can identify SSRs in DNA sequences. It can be used to analyze genomic and transcriptomic sequences from various organisms and can identify perfect, compound, and imperfect SSRs.
Primer3: Primer3 is a web-based tool that designs PCR primers for SSRs. It can design primers for perfect and imperfect SSRs, and can be used to design primers for SSRs in various organisms.
QDD: QDD (Quick Detection of Duplication) is a software tool that can identify SSRs in DNA sequences and can also identify duplicate loci. It can be used to analyze genomic and transcriptomic sequences from various organisms.

These are just a few examples of the many bioinformatics tools available for exploring SSRs. Depending on your specific needs and research questions, you may find that other tools are more appropriate for your analysis.

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/