<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44559?offset=280 https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats Thu, 09 Mar 2023 02:40:52 -0600 https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats <![CDATA[Common methods to discover tandem repeats]]>

Tandem repeats are DNA sequences that are repeated in a contiguous manner in the genome. These sequences are often used as genetic markers and are important in many areas of genetics and genomics research. Here are some methods for discovering tandem repeats in genomes:

  1. Tandem Repeat Finder: Tandem Repeat Finder is a software tool that identifies tandem repeats in DNA sequences. It is available for free download and can be used on both nucleotide and protein sequences. The tool uses a statistical algorithm to identify repeats based on their length, copy number, and overall composition.

  2. RepeatMasker: RepeatMasker is another software tool that can identify tandem repeats in DNA sequences. It works by comparing the input sequence to a database of known repeats and then identifies any tandem repeats that match those in the database.

  3. PCR-based methods: Polymerase chain reaction (PCR) can be used to amplify and detect tandem repeats in genomic DNA. PCR primers are designed to flank the tandem repeat region, and amplification of the target DNA fragment can be visualized on a gel. This method can be useful for detecting novel tandem repeats and for genotyping.

  4. Southern blotting: Southern blotting is a classic method for detecting DNA fragments in a sample. It can be used to detect tandem repeats by digesting genomic DNA with a restriction enzyme, separating the fragments by gel electrophoresis, and then probing the blot with a tandem repeat-specific probe.

Overall, a combination of these methods can be used to comprehensively identify tandem repeats in genomes.

]]> BioStar https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools Tue, 20 Nov 2018 03:54:26 -0600 https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools <![CDATA[List of motif discovery tools !]]>
In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. For proteins, a sequence motif is distinguished from a structural motif, a motif formed by the three-dimensional arrangement of amino acids which may not be adjacent.
 
Following are the list of tools for motif discovery:
 

Perform secondary structure predictions on protein sequences.

Find binding specificity information about DNA-protein complexes.

Find information about the binding specificity of DNA-binding proteins.

Use this web-based sequence motif visualization system to display sequence motif information in its appropriate three-dimensional (3D) context.

Protein function prediction and annotation in an integrated environment powered by web service.

Use to predict function from de novo protein sequences.

Search for short active protein sequences with demonstrated biological activities.

Search for ungapped segments corresponding to the most highly conserved regions of proteins.

Identify and measure surface accessible pockets as well as interior inaccessible cavities, for proteins and other molecules.

To search for catalytic residue annotation for enzymes in the Protein Data Bank.

Predict protein function using Gene Ontology.

Automatically calculate evolutionary conservation scores of key amino acid residues and map them on protein structures.

Mine the protein structure space.

Predict short linear motifs (3-8 residues) in a set of protein sequences.

A web client for visualizing protein sequence feature information using DAS.

Identify the domain architecture within a protein sequence.

Predict functional sites in eukaryotic proteins.

Use a collection of tools for protein analyses.

Predict potential protein post-translational modifications and find potential single amino acid substitutions in peptides.

Search for information related to the catalytic mechanisms of enzymes.

An integrated feature-based function prediction server for vertebrate proteomes.

Identify the closest matching PRINTS sequence motif fingerprints in a protein sequence.

Search for functional annotation of important sites in proteins with known structures.

Produce 3D conformations of small drug compounds.

A database presenting experiment-based results in human proteomics.

Conduct exhaustive intermediate profile searches of a set of homologous protein sequences.

Design protein mutations in site-directed mutagenesis.

Annotate protein using this integrated annotation resource.

Identify protein family (and DNA) domains, patterns, motifs, protein families, and functional sites.

Interactive forecasting of protein interaction hot spots.

Database containing pairs of structural analogs and their alignments.

Find sequence patterns in DNA and protein sequences.

A web server for knowledge-based modeling of protein-peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases.

Find structural segments or motifs for protein structures.

Visualize protein sequence motifs on the 3D protein structures.

Find presence of any known protein motif (Prosite and Pfam) in a protein sequence.

Recognize spatial chemical binding patterns common to a set of protein structures.

Analyze proteins for the presence of N-terminal N-myristoylation site.

Find the presence of N-Glycosylation sites in human proteins.

Find the presence of O-GalNAc (mucin type) glycosylation sites in mammalian proteins.

Analyze eukaryotic proteins for the presence of serine, threonine and tyrosine phosphorylation sites.

Find possible kinase specific phosphorylation sites in eukaryotic proteins.

Predict cleavage sites at basic amino acid locations in neuropeptide precursor sequences.

Find information about patented nucleotide and protein sequences.

Search for information about glycoproteins with O-linked and C-linked glycosylation sites.

Find information about protein sequence annotations.

A server to predict protein active site residues.

Search for structural and functional information on the protein functional sites.

Search 3D protein fragments similar in structure to known active, binding and posttranslational modification sites.

Conduct genome wide functional and structural analysis.

Search for phosphorylation data of any protein of interest.

Search for information on prokaryotic proteins that undergo serine, threonine, or tyrosine phosphorylation.

Determine correct names for proteins.

Web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.

Find homologous protein-protein interactions across multiple species.

Search your query sequence against PROSITE pattern database for protein motifs.

Find information about protein-RNA complexes from the Protein Data Bank (PDB).

Identify protein families and domains for a given protein sequence.

A comprehensive database of pattern-recognition receptors and their ligands.

Search for short nucleotide or peptide sequences such as cis-elements in nucleotide sequences or small domains and motifs in protein sequences.

Database specialized in documenting human PPBD-containing proteins and PPBD-mediated interactions.

Predicts potential protease cleavage sites and sites cleaved by chemicals in a given protein sequence.

Predict combined transmembrane topology and signal peptides.

Search for eukaryotic phosphorylation sites.

Search for 3D structure and functional annotation of phosphorylation sites in proteins.

Search the database of in vivo phosphorylation sites of human and mouse proteins

Find information about polyglutamine (polyQ) repeats.

Find the presence of protein motifs and patterns in an amino acid sequence.

Predict signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences.

Predict protein functions based on known structures.

Predict the location of potential protein-protein binding sites for unbound proteins.

Identify short linear signatures in protein termini.

Analyze and identify newly obtained protein sequences.

Predict protein binding sites in a protein sequence based on geometrical analysis of protein tertiary substructures.

Search for evolutionarily conserved motif-like patterns in protein sequences.

Web-based server for analyzing and predicting RNA binding sites in proteins.

Search for similarities between proteins by simultaneous matching of multiple motifs.

Predict residues in protein sequences that determine the proteins' functional specificity.

Predict specificity-determining residues in protein families.

Find shared motifs in proteins with a common attribute.

Conduct in silico sumoylation sites prediction.

Search for motifs and patterns within protein sequences.

Search for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains such as SH2 domains, 14-3-3 domains or PDZ domains.

Find information about populations carrying polymorphisms within protein binding pockets that make them susceptible to serious adverse drug reaction (SADR).

Search the presence of a motif in either amino acid sequence or nucleotide sequence.

Predict the presence of tyrosine sulfation sites in protein sequences

Look at protein structure from a ligand and binding site perspective.

Use a collection of bioinformatics tools at this portal site.

Find information about tandem repeats in proteins that carry fundamental biological functions and are related to a number of human diseases.

Find information about functional residues in alpha-helical and beta-barrel membrane proteins.

Database of domains and motifs with conservative location in transmembrane proteins.

Search for highly conserved and specific protein sequence motifs.

Predict functional sites in protein sequence alignments use different methodologies.

Find de novo protein motifs from chromatin immunoprecipitation data.

Scan query structures for functional sites in both proteins and nucleic acids.

Analyze quantitative structure-activity relationship of related protein families.

Search for ungapped alignments of highly conserved regions among a protein family or superfamily.

An expert system for predicting ligand-binding residues in protein structures.

Automatically identify spatially interacting motifs among distantly related proteins sharing similar folds and possessing common ancestral lineage.

]]> Neel https://bioinformaticsonline.com/bookmarks/view/35119/frontend-perl-web-framework-documentation-andrej-sali-lab Mon, 08 Jan 2018 22:32:03 -0600 https://bioinformaticsonline.com/bookmarks/view/35119/frontend-perl-web-framework-documentation-andrej-sali-lab <![CDATA[Frontend: Perl Web framework documentation - Andrej Sali Lab]]> 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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38457/pilongrid-parallel-wrapper-around-the-pilon-framework Thu, 13 Dec 2018 09:35:40 -0600 https://bioinformaticsonline.com/bookmarks/view/38457/pilongrid-parallel-wrapper-around-the-pilon-framework <![CDATA[PilonGrid: parallel wrapper around the Pilon framework]]> The distribution is a parallel wrapper around the Pilon framework The pipeline is composed of bash scripts, an example mapping.fofn which shows how to input your fastq files (you give paths to the R1 file), and how to launch the pipeline.

 

Address of the bookmark: https://github.com/skoren/PilonGrid

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/40140/alf-a-simulation-framework-for-genome-evolution Tue, 22 Oct 2019 22:05:58 -0500 https://bioinformaticsonline.com/bookmarks/view/40140/alf-a-simulation-framework-for-genome-evolution <![CDATA[ALF--a simulation framework for genome evolution.]]> Artificial Life Framework (ALF) simulates a root genome into a number of related genomes. Result files include the resulting gene sequences, true tree and true MSAs. A description of ALF can be found in the following article:

Daniel A Dalquen, Maria Anisimova, Gaston H Gonnet, Christophe Dessimoz: ALF - A Simulation Framework for Genome Evolution. Mol Biol Evol, 29(4):1115-1123, April 2012.
http://mbe.oxfordjournals.org/content/29/4/1115

Address of the bookmark: http://alfsim.org/#index

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37618/snakemake%E2%80%94a-scalable-bioinformatics-workflow-engine Sun, 02 Sep 2018 16:32:42 -0500 https://bioinformaticsonline.com/bookmarks/view/37618/snakemake%E2%80%94a-scalable-bioinformatics-workflow-engine <![CDATA[Snakemake—a scalable bioinformatics workflow engine]]> Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. 

Address of the bookmark: https://bioconda.github.io/recipes/snakemake/README.html

]]> Jit https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os Sun, 06 Apr 2014 23:56:18 -0500 https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os <![CDATA[Find certain files/documents in Linux OS]]> As bioinformatician I know the fact that we usually handle the large dataset and lost in the huge numbers of files and folders. In order to search the missing file a strong search command is required. The Linux Find Command is one of the most important and much used command in Linux sytems. Find command used to search and locate list of files and directories based on conditions you specify for files that match the arguments. Find can be used in variety of conditions like you can find files by permissions, users, groups, file type, date, size and other possible criteria.

Through this article we are sharing our day-to-day Linux find command experience and its usage in the form of examples. In this article we will show you the most used 35 Find Commands examples in Linux. We have divided the section into Five parts from basic to advance usage of find command.

Part I – Basic Find Commands for Finding Files with Names
1. Find Files Using Name in Current Directory

Find all the files whose name is gene.txt in a current working directory.

# find . -name gene.txt

./gene.txt

2. Find Files Under Home Directory

Find all the files under /home directory with name gene.txt.

# find /home -name gene.txt

/home/gene.txt

3. Find Files Using Name and Ignoring Case

Find all the files whose name is gene.txt and contains both capital and small letters in /home directory.

# find /home -iname gene.txt

./gene.txt
./Gene.txt

4. Find Directories Using Name

Find all directories whose name is Gene in / directory.

# find / -type d -name Gene

/Gene

5. Find fasta Files Using Name

Find all php files whose name is gene.fasta in a current working directory.

# find . -type f -name gene.fasta

./gene.fasta

6. Find all PHP Files in Directory

Find all fasta files in a directory.

# find . -type f -name "*.fasta"

./gene.fasta
./cancer.fasta
./allgene.fasta

Part II – Find Files Based on their Permissions
7. Find Files With 777 Permissions

Find all the files whose permissions are 777.

# find . -type f -perm 0777 -print

8. Find Files Without 777 Permissions

Find all the files without permission 777.

# find / -type f ! -perm 777

9. Find SGID Files with 644 Permissions

Find all the SGID bit files whose permissions set to 644.

# find / -perm 2644

10. Find Sticky Bit Files with 551 Permissions

Find all the Sticky Bit set files whose permission are 551.

# find / -perm 1551

11. Find SUID Files

Find all SUID set files.

# find / -perm /u=s

12. Find SGID Files

Find all SGID set files.

# find / -perm /g+s

13. Find Read Only Files

Find all Read Only files.

# find / -perm /u=r

14. Find Executable Files

Find all Executable files.

# find / -perm /a=x

15. Find Files with 777 Permissions and Chmod to 644

Find all 777 permission files and use chmod command to set permissions to 644.

# find / -type f -perm 0777 -print -exec chmod 644 {} \;

16. Find Directories with 777 Permissions and Chmod to 755

Find all 777 permission directories and use chmod command to set permissions to 755.

# find / -type d -perm 777 -print -exec chmod 755 {} \;

17. Find and remove single File

To find a single file called gene.txt and remove it.

# find . -type f -name "gene.txt" -exec rm -f {} \;

18. Find and remove Multiple File

To find and remove multiple files such as .fa or .gb, then use.

# find . -type f -name "*.fa" -exec rm -f {} \;

OR

# find . -type f -name "*.gb" -exec rm -f {} \;

19. Find all Empty Files

To file all empty files under certain path.

# find /tmp -type f -empty

20. Find all Empty Directories

To file all empty directories under certain path.

# find /tmp -type d -empty

21. File all Hidden Files

To find all hidden files, use below command.

# find /tmp -type f -name ".*"

Part III – Search Files Based On Owners and Groups
22. Find Single File Based on User

To find all or single file called gene.txt under / root directory of owner root.

# find / -user root -name gene.txt

23. Find all Files Based on User

To find all files that belongs to user Rahul under /home directory.

# find /home -user rahul

24. Find all Files Based on Group

To find all files that belongs to group Developer under /home directory.

# find /home -group developer

25. Find Particular Files of User

To find all .txt files of user Rahul under /home directory.

# find /home -user rahul -iname "*.txt"

Part IV – Find Files and Directories Based on Date and Time
26. Find Last 50 Days Modified Files

To find all the files which are modified 50 days back.

# find / -mtime 50

27. Find Last 50 Days Accessed Files

To find all the files which are accessed 50 days back.

# find / -atime 50

28. Find Last 50-100 Days Modified Files

To find all the files which are modified more than 50 days back and less than 100 days.

# find / -mtime +50 –mtime -100

29. Find Changed Files in Last 1 Hour

To find all the files which are changed in last 1 hour.

# find / -cmin -60

30. Find Modified Files in Last 1 Hour

To find all the files which are modified in last 1 hour.

# find / -mmin -60

31. Find Accessed Files in Last 1 Hour

To find all the files which are accessed in last 1 hour.

# find / -amin -60

Part V – Find Files and Directories Based on Size
32. Find 50MB Files

To find all 50MB files, use.

# find / -size 50M

33. Find Size between 50MB – 100MB

To find all the files which are greater than 50MB and less than 100MB.

# find / -size +50M -size -100M

34. Find and Delete 100MB Files

To find all 100MB files and delete them using one single command.

# find / -size +100M -exec rm -rf {} \;

35. Find Specific Files and Delete

Find all .gb files with more than 10MB and delete them using one single command.

# find / -type f -name *.gb -size +10M -exec rm {} \;

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search Tue, 28 Jan 2020 03:34:23 -0600 https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search <![CDATA[Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search]]> Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only

Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles.  Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.

https://arxiv.org/abs/1809.06700

https://github.com/redayounsi/2KP2P

/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1

 

Address of the bookmark: https://github.com/redayounsi/2KP2P

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing Mon, 06 Aug 2018 17:19:15 -0500 https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing <![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]> gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner. 

Address of the bookmark: http://ml.ssu.ac.kr/gSearch/index.html

]]> Jit https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech Thu, 10 Jan 2019 04:42:06 -0600 https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech <![CDATA[NGS Platforms launched by BGI’s MGI Tech]]> MGI Tech Co., Ltd. (MGI), a subsidiary of BGI Group, is committed to enabling effective and affordable healthcare solutions for all. Based on its proprietary technology, MGI produces sequencing devices, equipment, consumables and reagents to support life science research, medicine and healthcare. MGI's multi-omics platforms include genetic sequencing, mass spectrometry and medical imaging. Providing real-time, comprehensive, life-long solutions, its mission is to develop and promote advanced life science tools for future healthcare.

MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to $5 per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.

In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for $1 million.

The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.

Powered by DNBseq™, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.

SOURCE MGI

https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/

http://en.mgitech.cn/

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