<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38670?offset=140</link>
	<atom:link href="https://bioinformaticsonline.com/related/38670?offset=140" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</guid>
	<pubDate>Thu, 24 May 2018 10:06:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</link>
	<title><![CDATA[pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file]]></title>
	<description><![CDATA[pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.<p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/pbalign" rel="nofollow">https://github.com/PacificBiosciences/pbalign</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</guid>
	<pubDate>Thu, 25 Oct 2018 04:48:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</link>
	<title><![CDATA[RopeBWT2: Incremental construction of FM-index for DNA sequences]]></title>
	<description><![CDATA[<p><span>RopeBWT2 is an tool for constructing the FM-index for a collection of DNA sequences. It works by incrementally inserting one or multiple sequences into an existing pseudo-BWT position by position, starting from the end of the sequences. This algorithm can be largely considered a mixture of&nbsp;</span><a href="http://dx.doi.org/10.1007/978-3-642-21458-5_20">BCR</a><span>&nbsp;and&nbsp;</span><a href="http://dfmi.sourceforge.net/">dynamic FM-index</a><span>. Nonetheless, ropeBWT2 is unique in that it may&nbsp;</span><em>implicitly</em><span>sort the input into reverse lexicographical order (RLO) or reverse-complement lexicographical order (RCLO) while building the index.</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/ropebwt2" rel="nofollow">https://github.com/lh3/ropebwt2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38678/upho-scripts-for-homology-and-orthology-assessment-from-genomic-sequences</guid>
	<pubDate>Mon, 14 Jan 2019 10:36:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38678/upho-scripts-for-homology-and-orthology-assessment-from-genomic-sequences</link>
	<title><![CDATA[UPhO: Scripts for homology and orthology assessment from genomic sequences.]]></title>
	<description><![CDATA[<p>UPhO finds orthologs with and without inparalogs from input gene family trees. Refer to the Documentation.pdf for more detailed explanations on its usage, installation and dependencies. Type UPhO.py -h for help.</p>
<p>The only input requierement for UPhO is a tree (or trees) in Newick format in which the leaves are named with a species idenfifier, a field separator, and sequence identifier. By default, the field separator is the character "|" but custom delimiters can be defined. Examples of trees to test UPhO are provided in the TestData folder.</p><p>Address of the bookmark: <a href="https://github.com/ballesterus/UPhO" rel="nofollow">https://github.com/ballesterus/UPhO</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/41230/curated-set-of-ribosomal-rna-rrna-reference-sequences-targeted-loci-with-verifiable-organism</guid>
	<pubDate>Sun, 23 Feb 2020 02:17:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/41230/curated-set-of-ribosomal-rna-rrna-reference-sequences-targeted-loci-with-verifiable-organism</link>
	<title><![CDATA[Curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism]]></title>
	<description><![CDATA[<p>MCBI have a curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples. To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms. The new databases are: </p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *16S ribosomal RNA (Bacteria and Archaea)</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *18S ribosomal RNA sequences (SSU) from Fungi type and reference material&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *28S ribosomal RNA sequences (LSU) from Fungi type and reference material</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *Internal transcribed spacer region (ITS) from Fungi type and reference material</p><p>You can also download these from the BLAST db FTP area.&nbsp; See the <a href="https://go.usa.gov/xdEBX" target="_blank">NCBI Insights post</a> for more detail. </p><p>Useful links</p><p>-----------------</p><p><a href="https://go.usa.gov/xdEj5" target="_blank">BLAST form with rRNA/ITS databases</a></p><p><a href="https://ftp.ncbi.nlm.nih.gov/blast/db/" target="_blank">BLAST db download</a></p><p><a href="https://www.ncbi.nlm.nih.gov/refseq/targetedloci/" target="_blank">Targeted loci</a></p><p><span style="color: black;">If you have any questions or concerns, please contact <a href="mailto:blast-help@ncbi.nlm.nih.gov" target="_blank" title="Follow link">blast-help@ncbi.nlm.nih.gov<sup><span style="color: black; text-decoration: none;"><img src="https://mail.google.com/mail/u/0?ui=2&amp;ik=024a8aa0b9&amp;attid=0.1&amp;permmsgid=msg-f:1659255165855446848&amp;th=1706dbc8408bb740&amp;view=fimg&amp;sz=s0-l75-ft&amp;attbid=ANGjdJ_drW2ArYDNLoHrQh36gm6rp2Std8ZUSplCzP6bYQSQYBsQfZ_85vOujXOdTRdaLxrR7QeEBVUbyACPBJHhFUeIglX8G7Ew7TcclzhvO7fJhiz7sIdkkDgZ7QA&amp;disp=emb" alt="https://jira.ncbi.nlm.nih.gov/images/icons/mail_small.gif" width="13" height="12" style="border: 0px;"></span></sup></a></span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</guid>
	<pubDate>Fri, 04 Mar 2022 00:14:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</link>
	<title><![CDATA[kebabs: package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods]]></title>
	<description><![CDATA[<p><span>The&nbsp;</span><tt>kebabs</tt><span>&nbsp;package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences. The package implements some of the most important kernels for sequence analysis in a very flexible and efficient way and extends the standard position-independent functionality of these kernels in a novel way to take the position of patterns in the sequences into account for the similarity measure.</span></p>
<p>http://www.bioinf.jku.at/software/kebabs/</p>
<p>http://bioconductor.org/packages/release/bioc/vignettes/kebabs/inst/doc/kebabs.pdf</p><p>Address of the bookmark: <a href="http://www.bioinf.jku.at/software/kebabs/" rel="nofollow">http://www.bioinf.jku.at/software/kebabs/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44616/basics-of-blast-programs</guid>
	<pubDate>Fri, 26 Jul 2024 06:04:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44616/basics-of-blast-programs</link>
	<title><![CDATA[Basics of BLAST Programs !]]></title>
	<description><![CDATA[<p>The Basic Local Alignment Search Tool (BLAST) is a powerful bioinformatics program used to compare an input sequence (such as DNA, RNA, or protein sequences) against a database of sequences to find regions of similarity. Developed by the National Center for Biotechnology Information (NCBI), BLAST is widely used for identifying species, finding functional and evolutionary relationships between sequences, and predicting the function of novel sequences.</p><p>Key Features of BLAST:<br />1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.</p><p>2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.</p><p>3. Versatility: There are several versions of BLAST for different types of sequence comparisons:<br /> - blastn: Compares a nucleotide query sequence against a nucleotide sequence database.<br /> - blastp: Compares a protein query sequence against a protein sequence database.<br /> - blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.<br /> - tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.<br /> - tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.</p><p>4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.</p><p>5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.</p><p>Applications of BLAST:<br />- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.<br />- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.<br />- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.<br />- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.</p><p>Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39017/macse-multiple-alignment-of-coding-sequences-accounting-for-frameshifts-and-stop-codons</guid>
	<pubDate>Mon, 18 Feb 2019 04:21:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39017/macse-multiple-alignment-of-coding-sequences-accounting-for-frameshifts-and-stop-codons</link>
	<title><![CDATA[MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons]]></title>
	<description><![CDATA[<p>MACSE aligns coding NT sequences with respect to their AA translation while allowing NT sequences to contain multiple frameshifts and/or stop codons. MACSE is hence the first automatic solution to align protein-coding gene datasets containing non-functional sequences (pseudogenes) without disrupting the underlying codon structure. It has also proved useful in detecting undocumented frameshifts in public database sequences and in aligning next-generation sequencing reads/contigs against a reference coding sequence.</p>
<p>For further details about the underlying algorithm see the original publication:<br><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022594" target="_new">MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons.<br>Vincent Ranwez, S&eacute;bastien Harispe, Fr&eacute;d&eacute;ric Delsuc, Emmanuel JP Douzery<br>PLoS One 2011, 6(9): e22594</a>.</p><p>Address of the bookmark: <a href="https://bioweb.supagro.inra.fr/macse/index.php?menu=releases" rel="nofollow">https://bioweb.supagro.inra.fr/macse/index.php?menu=releases</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39019/iq-tree-efficient-software-for-phylogenomic-inference</guid>
	<pubDate>Mon, 18 Feb 2019 04:25:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39019/iq-tree-efficient-software-for-phylogenomic-inference</link>
	<title><![CDATA[IQ-TREE: Efficient software for phylogenomic inference]]></title>
	<description><![CDATA[<p><span>A fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood.&nbsp;</span><em>IQ-TREE compares favorably to RAxML and PhyML</em><span>&nbsp;in terms of likelihoods with similar computing time</span></p>
<p><span><span>IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3&ndash;97.1%. IQ-TREE is freely available at&nbsp;</span><a href="http://www.cibiv.at/software/iqtree" target="">http://www.cibiv.at/software/iqtree</a></span></p><p>Address of the bookmark: <a href="http://www.iqtree.org/" rel="nofollow">http://www.iqtree.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os</guid>
	<pubDate>Sun, 06 Apr 2014 23:56:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os</link>
	<title><![CDATA[Find certain files/documents in Linux OS]]></title>
	<description><![CDATA[<p>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.<br /><br />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.</p><p><strong>Part I &ndash; Basic Find Commands for Finding Files with Names</strong><br />1. Find Files Using Name in Current Directory<br /><br />Find all the files whose name is gene.txt in a current working directory.<br /><br /># find . -name gene.txt<br /><br />./gene.txt<br /><br />2. Find Files Under Home Directory<br /><br />Find all the files under /home directory with name gene.txt.<br /><br /># find /home -name gene.txt<br /><br />/home/gene.txt<br /><br />3. Find Files Using Name and Ignoring Case<br /><br />Find all the files whose name is gene.txt and contains both capital and small letters in /home directory.<br /><br /># find /home -iname gene.txt<br /><br />./gene.txt<br />./Gene.txt<br /><br />4. Find Directories Using Name<br /><br />Find all directories whose name is Gene in / directory.<br /><br /># find / -type d -name Gene<br /><br />/Gene<br /><br />5. Find fasta Files Using Name<br /><br />Find all php files whose name is gene.fasta in a current working directory.<br /><br /># find . -type f -name gene.fasta<br /><br />./gene.fasta<br /><br />6. Find all PHP Files in Directory<br /><br />Find all fasta files in a directory.<br /><br /># find . -type f -name "*.fasta"<br /><br />./gene.fasta<br />./cancer.fasta<br />./allgene.fasta<br /><br /><strong>Part II &ndash; Find Files Based on their Permissions</strong><br />7. Find Files With 777 Permissions<br /><br />Find all the files whose permissions are 777.<br /><br /># find . -type f -perm 0777 -print<br /><br />8. Find Files Without 777 Permissions<br /><br />Find all the files without permission 777.<br /><br /># find / -type f ! -perm 777<br /><br />9. Find SGID Files with 644 Permissions<br /><br />Find all the SGID bit files whose permissions set to 644.<br /><br /># find / -perm 2644<br /><br />10. Find Sticky Bit Files with 551 Permissions<br /><br />Find all the Sticky Bit set files whose permission are 551.<br /><br /># find / -perm 1551<br /><br />11. Find SUID Files<br /><br />Find all SUID set files.<br /><br /># find / -perm /u=s<br /><br />12. Find SGID Files<br /><br />Find all SGID set files.<br /><br /># find / -perm /g+s<br /><br />13. Find Read Only Files<br /><br />Find all Read Only files.<br /><br /># find / -perm /u=r<br /><br />14. Find Executable Files<br /><br />Find all Executable files.<br /><br /># find / -perm /a=x<br /><br />15. Find Files with 777 Permissions and Chmod to 644<br /><br />Find all 777 permission files and use chmod command to set permissions to 644.<br /><br /># find / -type f -perm 0777 -print -exec chmod 644 {} \;<br /><br />16. Find Directories with 777 Permissions and Chmod to 755<br /><br />Find all 777 permission directories and use chmod command to set permissions to 755.<br /><br /># find / -type d -perm 777 -print -exec chmod 755 {} \;<br /><br />17. Find and remove single File<br /><br />To find a single file called gene.txt and remove it.<br /><br /># find . -type f -name "gene.txt" -exec rm -f {} \;<br /><br />18. Find and remove Multiple File<br /><br />To find and remove multiple files such as .fa or .gb, then use.<br /><br /># find . -type f -name "*.fa" -exec rm -f {} \;<br /><br />OR<br /><br /># find . -type f -name "*.gb" -exec rm -f {} \;<br /><br />19. Find all Empty Files<br /><br />To file all empty files under certain path.<br /><br /># find /tmp -type f -empty<br /><br />20. Find all Empty Directories<br /><br />To file all empty directories under certain path.<br /><br /># find /tmp -type d -empty<br /><br />21. File all Hidden Files<br /><br />To find all hidden files, use below command.<br /><br /># find /tmp -type f -name ".*"<br /><br /><strong>Part III &ndash; Search Files Based On Owners and Groups</strong><br />22. Find Single File Based on User<br /><br />To find all or single file called gene.txt under / root directory of owner root.<br /><br /># find / -user root -name gene.txt<br /><br />23. Find all Files Based on User<br /><br />To find all files that belongs to user Rahul under /home directory.<br /><br /># find /home -user rahul<br /><br />24. Find all Files Based on Group<br /><br />To find all files that belongs to group Developer under /home directory.<br /><br /># find /home -group developer<br /><br />25. Find Particular Files of User<br /><br />To find all .txt files of user Rahul under /home directory.<br /><br /># find /home -user rahul -iname "*.txt"<br /><br /><strong>Part IV &ndash; Find Files and Directories Based on Date and Time</strong><br />26. Find Last 50 Days Modified Files<br /><br />To find all the files which are modified 50 days back.<br /><br /># find / -mtime 50<br /><br />27. Find Last 50 Days Accessed Files<br /><br />To find all the files which are accessed 50 days back.<br /><br /># find / -atime 50<br /><br />28. Find Last 50-100 Days Modified Files<br /><br />To find all the files which are modified more than 50 days back and less than 100 days.<br /><br /># find / -mtime +50 &ndash;mtime -100<br /><br />29. Find Changed Files in Last 1 Hour<br /><br />To find all the files which are changed in last 1 hour.<br /><br /># find / -cmin -60<br /><br />30. Find Modified Files in Last 1 Hour<br /><br />To find all the files which are modified in last 1 hour.<br /><br /># find / -mmin -60<br /><br />31. Find Accessed Files in Last 1 Hour<br /><br />To find all the files which are accessed in last 1 hour.<br /><br /># find / -amin -60<br /><br /><strong>Part V &ndash; Find Files and Directories Based on Size</strong><br />32. Find 50MB Files<br /><br />To find all 50MB files, use.<br /><br /># find / -size 50M<br /><br />33. Find Size between 50MB &ndash; 100MB<br /><br />To find all the files which are greater than 50MB and less than 100MB.<br /><br /># find / -size +50M -size -100M<br /><br />34. Find and Delete 100MB Files<br /><br />To find all 100MB files and delete them using one single command.<br /><br /># find / -size +100M -exec rm -rf {} \;<br /><br />35. Find Specific Files and Delete<br /><br />Find all .gb files with more than 10MB and delete them using one single command.<br /><br /># find / -type f -name *.gb -size +10M -exec rm {} \;</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43398/waafle-a-workflow-to-annotate-assemblies-and-find-lgt-events</guid>
	<pubDate>Thu, 23 Sep 2021 14:31:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43398/waafle-a-workflow-to-annotate-assemblies-and-find-lgt-events</link>
	<title><![CDATA[WAAFLE: a Workflow to Annotate Assemblies and Find LGT Events.]]></title>
	<description><![CDATA[<p><span>Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial communities, including the human microbiome. While methods exist to identify LGTs from sequenced isolate genomes, identifying LGTs from community metagenomes remains an open problem. To address this, we developed&nbsp;</span><span>WAAFLE</span><span>: a&nbsp;</span><span>W</span><span>orkflow to&nbsp;</span><span>A</span><span>nnotate&nbsp;</span><span>A</span><span>ssemblies and&nbsp;</span><span>F</span><span>ind&nbsp;</span><span>L</span><span>GT&nbsp;</span><span>E</span><span>vents.</span></p><p>Address of the bookmark: <a href="http://huttenhower.sph.harvard.edu/waafle" rel="nofollow">http://huttenhower.sph.harvard.edu/waafle</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

</channel>
</rss>