BOL: Related items

Search Shell Command History

Rahul Nayak — Thu, 12 Jun 2014 17:43:34 -0500

We use couple of hundreads of command in daily basis. Most of them are actually repeated several time. The question remain open how do I search old command history under bash shell and modify or reuse it?

Now a days almost all modern shell allows you to search command history if enabled by user. Use history command to display the history list with line numbers. Lines listed with with a * have been modified by user.

Shell history search command

Type history at a shell prompt:
$ history

It will display the list of all used commandline history with an serial number.

To search particular command, enter:
$ history | grep command-name
$ history | egrep -i 'scp|ssh|ftp'
Emacs Line-Edit Mode Command History Searching

To get previous command containing string, hit [CTRL]+[r] followed by search string:

(reverse-i-search):

To get previous command, hit [CTRL]+[p]. You can also use up arrow key.

CTRL-p

To get next command, hit [CTRL]+[n]. You can also use down arrow key.

CTRL-n

fc command

Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.

For example list last 10 command, enter:
$ fc -l 10
To list commands 130 through 150, enter:
$ fc -l 130 150
To list all commands since the last command beginning with ssh, enter:
$ fc -l ssh
You can edit commands 1 through 5 using vi text editor, enter:
$ fc -e vi 1 5

Delete command history

The -c option causes the history list to be cleared by deleting all of the entries:
$ history -c

MCAT: Motif Combining and Association Tool

Neel — Sun, 13 Jan 2019 06:27:28 -0600

This is a pipeline for finding motifs in fasta files.
It can be run from the command line as follows:

usage: orange_pipeline_refine.py [-h] [-w W] [--nmotifs NMOTIFS] [--iter ITER] [-c C]
[-s S] [-d] [-ff] [-v V]
positive_seq negative_seq

positional arguments:
positive_seq the fasta file for the positive sequences
negative_seq the fasta file for the negative sequences

Address of the bookmark: https://github.com/yanshen43/MCAT

Common steps for reads mapping !

BioStar — Thu, 09 Mar 2023 02:48:02 -0600

Mapping reads to a reference genome is an essential step in many types of genomic analysis, such as variant calling and gene expression analysis. Here are some general steps to follow for mapping reads to a genome:

Choose a read mapper: There are many read mappers available, such as BWA, Bowtie, and HISAT2. Choose a mapper that is appropriate for your type of data and research question.
Index the reference genome: Before mapping reads, the reference genome needs to be indexed. This involves creating an index of the genome sequence that allows the mapper to quickly find matches to the reads. Most mappers have their own indexing tools.
Prepare the read data: The reads should be in a format that is compatible with the mapper. Most mappers accept FASTQ or BAM files. Depending on the quality of the data, it may need to be filtered or trimmed before mapping.
Run the mapper: The mapper is run with the command-line interface or using a graphical user interface. The specific command depends on the mapper being used, but typically involves specifying the input data, reference genome, and output file format.
Evaluate the mapping results: After the mapping is complete, the results should be evaluated. This includes assessing the quality of the mapping, such as the mapping rate, the number of mapped reads, and the mapping quality score.
Post-processing: Depending on the analysis being performed, post-processing of the mapped reads may be necessary. This can include filtering reads based on quality, removing duplicate reads, and calling variants.

Overall, mapping reads to a reference genome is a complex process that requires careful consideration of the type of data, the research question, and the specific mapper being used.

ForBio and UiB course: Introduction to phylogenetic methods

Rahul Nayak — Mon, 16 Dec 2019 09:39:12 -0600

The is an introductory course that is aimed at students who will need to preform phylogenetic analyses in their work, but who have little or no experience with phylogenetic analyses. The ForBio component of the course is focused on the practical aspects of phylogenetic analyses and students that have not attended the theoretical part of the course are expected to have read the relevant literature. The course will cover all basic aspects of phylogenetic analyses with emphasis on use of DNA data. Students will learn how to prepare their data, explore its properties and how to analyze it using distance, parsimony, likelihood and Bayesian methods. In the last two days students will also get an introduction to molecular dating with focus on the use of BEAST and to comparative methods used to study the evolution of discrete and continuous traits.

https://www.forbio.uio.no/events/courses/2020/Bergen_Phylogenetics_2020.html

ForBio travel grants
We have changed our travel grants rules. ForBio offers both incoming and outgoing travel grants to fund collaborations between ForBio members based in Norway and taxonomic experts up to NOK 14,000. Grants are given for taxonomic training with relevance to the main research project of the applicant, and aim to allow the student to do taxonomic research with specific experts, work with particular collections, or learn new preparation techniques directly from experts. Participation in external courses, conferences, fieldwork or other kinds of activities which are not classified as expert-in-training visits are no longer eligible for support. There are two application deadlines: March 1st 2020.

RepeatModeler

Jit — Thu, 18 Aug 2016 09:57:15 -0500

RepeatModeler is a de-novo repeat family identification and modeling package. At the heart of RepeatModeler are two de-novo repeat finding programs ( RECON and RepeatScout ) which employ complementary computational methods for identifying repeat element boundaries and family relationships from sequence data. RepeatModeler assists in automating the runs of RECON and RepeatScout given a genomic database and uses the output to build, refine and classify consensus models of putative interspersed repeats.

Address of the bookmark: http://www.repeatmasker.org/RepeatModeler.html

Genome Stability Laboratory

Mon, 07 Mar 2016 04:16:32 -0600

The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims.

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

SISRS: Site Identification from Short Read Sequences

Abhimanyu Singh — Wed, 28 Nov 2018 08:56:03 -0600

Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.

Address of the bookmark: https://github.com/rachelss/SISRS

KAST

Neel — Wed, 23 Feb 2022 08:28:36 -0600

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

KAST: Perform Alignment-free k-tuple frequency comparisons from sequences

Jit — Thu, 20 Sep 2018 08:56:35 -0500

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R

BioStar — Sun, 09 Dec 2018 19:06:17 -0600

DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R programming language. The R package is distributed as platform independent source code under the GPL version 3 license. Some functionality of the program is accessible online through web tools.

Address of the bookmark: http://www2.decipher.codes/