<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40140?offset=220 https://bioinformaticsonline.com/blog/view/44703/the-role-of-lncrna-in-bioinformatics-unlocking-the-secrets-of-the-genome Sat, 07 Dec 2024 02:09:47 -0600 https://bioinformaticsonline.com/blog/view/44703/the-role-of-lncrna-in-bioinformatics-unlocking-the-secrets-of-the-genome <![CDATA[The Role of lncRNA in Bioinformatics: Unlocking the Secrets of the Genome]]> In the intricate dance of molecular biology, long non-coding RNAs (lncRNAs) have emerged as key players, capturing the interest of researchers worldwide. These RNA molecules, once dismissed as "junk," have proven to be vital in the regulation of gene expression, cellular processes, and the progression of diseases. The intersection of lncRNA studies and bioinformatics is transforming our understanding of these enigmatic molecules, offering profound insights into their structure, function, and therapeutic potential.

What Are lncRNAs?

lncRNAs are RNA transcripts longer than 200 nucleotides that do not code for proteins. Despite their non-coding nature, they play diverse roles in gene regulation, including chromatin remodeling, transcriptional control, and post-transcriptional processing. Unlike messenger RNAs (mRNAs), lncRNAs often function as scaffolds, decoys, or guides in cellular machinery, influencing biological processes such as cell differentiation, immune response, and even cancer metastasis.

Challenges in lncRNA Research

Identifying and understanding lncRNAs pose unique challenges:

  1. High Sequence Variability: Unlike protein-coding genes, lncRNAs exhibit low sequence conservation across species, making functional predictions difficult.
  2. Low Expression Levels: lncRNAs are often expressed at low levels, complicating their detection in transcriptomic data.
  3. Diverse Functions: The multifunctional nature of lncRNAs requires advanced computational tools to decipher their roles in complex networks.

Bioinformatics: A Crucial Ally in lncRNA Research

Bioinformatics bridges the gap between raw biological data and meaningful insights, making it indispensable in lncRNA research. Here’s how:

1. Identification and Annotation

High-throughput sequencing technologies like RNA-seq generate vast amounts of data. Bioinformatics tools such as StringTie, Cufflinks, and HISAT2 help assemble and annotate lncRNAs from this data. Additionally, databases like NONCODE, LNCipedia, and Ensembl provide curated repositories of lncRNA sequences and annotations.

2. Functional Prediction

Bioinformatics algorithms predict the potential functions of lncRNAs by analyzing their interactions with DNA, RNA, and proteins. Tools like LncRNA2Function and RIblast utilize sequence motifs and secondary structure predictions to hypothesize about the roles of specific lncRNAs.

3. Network Construction

lncRNAs often act as regulatory hubs. Bioinformatics platforms such as Cytoscape enable the visualization of lncRNA-mediated networks, elucidating their roles in pathways like cell cycle regulation and apoptosis.

4. Epigenetic Studies

lncRNAs are known to interact with chromatin-modifying complexes, influencing gene expression epigenetically. Tools like ChIP-seq and ATAC-seq, combined with computational pipelines, identify these interactions and map them to the genome.

5. Clinical Applications

Bioinformatics aids in the discovery of lncRNA biomarkers for diseases like cancer and neurodegenerative disorders. Machine learning models analyze differential expression profiles, helping prioritize lncRNAs with therapeutic potential.

Case Study: lncRNAs in Cancer Research

lncRNAs such as HOTAIR and MALAT1 have been implicated in cancer progression. Bioinformatics analyses have revealed their roles in promoting metastasis and altering the tumor microenvironment. For example, transcriptome analysis in cancer patients identifies lncRNA expression signatures, enabling precision medicine approaches.

Future Directions

The fusion of bioinformatics with experimental biology is unlocking the secrets of lncRNAs. Advances in artificial intelligence, single-cell sequencing, and structural modeling promise to overcome current limitations. Here are some promising directions:

  • Integrative Analysis: Combining multi-omics data to understand the interplay of lncRNAs with other biomolecules.
  • CRISPR Screens: Leveraging bioinformatics to design CRISPR-based functional screens for lncRNAs.
  • Therapeutic Development: Using bioinformatics to design lncRNA-based therapeutics, including antisense oligonucleotides and RNA interference tools.

Conclusion

lncRNAs are the hidden gems of the genome, and bioinformatics is the key to unearthing their full potential. As research progresses, lncRNAs could pave the way for novel diagnostics, targeted therapies, and personalized medicine, revolutionizing our approach to complex diseases.

The journey into the world of lncRNAs is only beginning, and bioinformatics will continue to play a pivotal role in decoding these molecular mysteries. Whether you’re a researcher, clinician, or bioinformatics enthusiast, the study of lncRNAs offers a fascinating frontier of discovery.

]]> LEGE https://bioinformaticsonline.com/blog/view/45251/aurora-a-new-detective-for-bacterial-genomes Fri, 21 Aug 2026 11:31:00 -0500 https://bioinformaticsonline.com/blog/view/45251/aurora-a-new-detective-for-bacterial-genomes <![CDATA[Aurora: A New Detective for Bacterial Genomes]]> Imagine trying to solve a mystery with thousands of clues—but some of the clues are labelled incorrectly.

That is the challenge researchers face when studying how bacteria adapt to different environments. A bacterial gene may appear to be linked to a particular habitat, when the real reason is simply that closely related bacteria happen to live there.

In their 2025 Genome Biology paper, Bujdoš, Walter, and O’Toole introduce aurora, a machine-learning tool designed to tackle this problem.

Aurora identifies potentially mislabeled or unusual bacterial strains before performing genome-wide association studies (GWAS). By cleaning up the dataset and accounting for bacterial evolutionary relationships, it can help researchers find genetic features that are more genuinely connected to habitat adaptation.

The researchers tested aurora using simulated and real bacterial datasets and found that it could recover important genetic associations even when datasets contained misleading labels.

The bigger lesson is simple: better biological discoveries often begin with better data.

Aurora gives researchers a new way to separate real genetic clues from misleading ones—and could help us better understand how bacteria adapt, survive, and evolve in the environments they call home.

Based on Bujdoš et al., “aurora: a machine learning GWAS tool for analyzing microbial habitat adaptation,” Genome Biology (2025).[Read the original paper](https://link.springer.com/article/10.1186/s13059-025-03524-7?)

]]> BioStar https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes Fri, 11 Sep 2026 00:51:02 -0500 https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes <![CDATA[The Tree That Learned to Read Genomes]]> Imagine trying to reconstruct a family tree when many family photographs are missing, some names are unclear, and different relatives remember the past differently. This is similar to the challenge scientists face when trying to understand how organisms are related. Their family records are genomes, and the family tree is a phylogenetic tree. With modern sequencing technologies, scientists now have access to thousands of genomes, but comparing them can be difficult, especially when genomes are incomplete or poorly annotated.

A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.

Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.

The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.

More at https://link.springer.com/article/10.1007/s10123-025-00752-6?

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

Interesting scripts within it

urbe@urbo214b[bin] ls                                                        []
-rwxrwxr-x 1 urbe urbe  11K Apr 10 11:41 addCNSToStore
-rwxrwxr-x 1 urbe urbe 575K Apr 10 11:41 addReadsToUnitigs
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-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 analyzeScaffolds
-rwxrwxr-x 1 urbe urbe 224K Apr 10 11:41 asmOutputFasta
-rwxrwxr-x 1 urbe urbe 448K Apr 10 11:41 asmOutputStatistics
-rwxrwxr-x 1 urbe urbe 2,4K Apr 10 11:41 asmToAGP.pl
-rwxrwxr-x 1 urbe urbe 7,6M Apr 10 11:41 blasr
-rwxrwxr-x 1 urbe urbe 1,6M Apr 10 11:41 bogart
-rwxrwxr-x 1 urbe urbe 183K Apr 10 11:41 bogus
-rwxrwxr-x 1 urbe urbe 272K Apr 10 11:41 bogusness
-rwxrwxr-x 1 urbe urbe 247K Apr 10 11:41 buildPosMap
-rwxrwxr-x 1 urbe urbe 213K Apr 10 11:41 buildRefContigs
-rwxrwxr-x 1 urbe urbe 990K Apr 10 11:41 buildUnitigs
-rwxrwxr-x 1 urbe urbe  18K Apr 10 11:41 ca2ace.pl
-rwxrwxr-x 1 urbe urbe  12K Apr 10 11:41 caqc_help.ini
-rwxrwxr-x 1 urbe urbe  61K Apr 10 11:41 caqc.pl
-rwxrwxr-x 1 urbe urbe  23K Apr 10 11:41 cat-corrects
-rwxrwxr-x 1 urbe urbe  24K Apr 10 11:41 cat-erates
-rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 cgw
-rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 cgwDump
-rwxrwxr-x 1 urbe urbe 204K Apr 10 11:41 chimChe
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 chimera
-rwxrwxr-x 1 urbe urbe 220K Apr 10 11:41 classifyMates
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:41 classifyMatesApply
-rwxrwxr-x 1 urbe urbe 215K Apr 10 11:41 classifyMatesPairwise
-rwxrwxr-x 1 urbe urbe 366K Apr 10 11:41 computeCoverageStat
-rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 convert-fasta-to-v2.pl
-rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 convertOverlap
-rwxrwxr-x 1 urbe urbe 119K Apr 10 11:41 convertSamToCA
-rwxrwxr-x 1 urbe urbe  20K Apr 10 11:41 convertToPBCNS
-rwxrwxr-x 1 urbe urbe 197K Apr 10 11:41 correct-frags
-rwxrwxr-x 1 urbe urbe 259K Apr 10 11:41 correct-olaps
-rwxrwxr-x 1 urbe urbe 520K Apr 10 11:41 correctPacBio
-rwxrwxr-x 1 urbe urbe 540K Apr 10 11:41 ctgcns
-rwxrwxr-x 1 urbe urbe 162K Apr 10 11:40 deduplicate
-rwxrwxr-x 1 urbe urbe  37K Apr 10 11:41 demotePosMap
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 dumpCloneMiddles
-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:41 dumpPBRLayoutStore
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 dumpSingletons
-rwxrwxr-x 1 urbe urbe 171K Apr 10 11:41 erate-estimate
-rwxrwxr-x 1 urbe urbe 221K Apr 10 11:40 estimate-mer-threshold
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 extendClearRanges
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 extendClearRangesPartition
-rwxrwxr-x 1 urbe urbe 205K Apr 10 11:40 extractmessages
-rwxrwxr-x 1 urbe urbe 7,2M Apr 10 11:41 falcon_sense
-rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 fastaToCA
-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:40 fastqAnalyze
-rwxrwxr-x 1 urbe urbe 137K Apr 10 11:40 fastqSample
-rwxrwxr-x 1 urbe urbe  62K Apr 10 11:40 fastqSimulate
-rwxrwxr-x 1 urbe urbe 121K Apr 10 11:40 fastqSimulate-sort
-rwxrwxr-x 1 urbe urbe 246K Apr 10 11:40 fastqToCA
-rwxrwxr-x 1 urbe urbe 140K Apr 10 11:41 filterOverlap
-rwxrwxr-x 1 urbe urbe 341K Apr 10 11:40 finalTrim
-rwxrwxr-x 1 urbe urbe 228K Apr 10 11:41 fixUnitigs
-rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 fragmentDepth
-rwxrwxr-x 1 urbe urbe  29K Apr 10 11:41 fragsInVars
-rwxrwxr-x 1 urbe urbe 545K Apr 10 11:41 frgs2clones
-rwxrwxr-x 1 urbe urbe 398K Apr 10 11:40 gatekeeper
-rwxrwxr-x 1 urbe urbe 139K Apr 10 11:40 gatekeeperbench
-rwxrwxr-x 1 urbe urbe 167K Apr 10 11:40 gkpStoreCreate
-rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 gkpStoreDumpFASTQ
-rwxrwxr-x 1 urbe urbe 184K Apr 10 11:41 greedyFragmentTiling
-rwxrwxr-x 1 urbe urbe 1,6K Apr 10 11:41 greedy_layout_to_IUM
-rwxrwxr-x 1 urbe urbe 142K Apr 10 11:40 initialTrim
-rwxrwxr-x 1 urbe urbe 967K Apr 10 11:41 jellyfish
-rwxrwxr-x 1 urbe urbe 219K Apr 10 11:41 markRepeatUnique
-rwxrwxr-x 1 urbe urbe 273K Apr 10 11:40 markUniqueUnique
-rwxrwxr-x 1 urbe urbe 114K Apr 10 11:40 mercy
-rwxrwxr-x 1 urbe urbe 3,8K Apr 10 11:41 mergeqc.pl
-rwxrwxr-x 1 urbe urbe 422K Apr 10 11:40 merTrim
-rwxrwxr-x 1 urbe urbe 125K Apr 10 11:40 merTrimApply
-rwxrwxr-x 1 urbe urbe 376K Apr 10 11:40 meryl
-rwxrwxr-x 1 urbe urbe 176K Apr 10 11:41 metagenomics_ovl_analyses
-rwxrwxr-x 1 urbe urbe 297K Apr 10 11:41 olap-from-seeds
-rwxrwxr-x 1 urbe urbe 275K Apr 10 11:41 outputLayout
-rwxrwxr-x 1 urbe urbe 229K Apr 10 11:41 overlapInCore
-rwxrwxr-x 1 urbe urbe 144K Apr 10 11:40 overlap_partition
-rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStats
-rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStore
-rwxrwxr-x 1 urbe urbe 153K Apr 10 11:41 overlapStoreBucketizer
-rwxrwxr-x 1 urbe urbe 175K Apr 10 11:41 overlapStoreBuild
-rwxrwxr-x 1 urbe urbe  33K Apr 10 11:41 overlapStoreIndexer
-rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 overlapStoreSorter
-rwxrwxr-x 1 urbe urbe 604K Apr 10 11:40 overmerry
lrwxrwxrwx 1 urbe urbe    4 Apr 10 11:41 pacBioToCA -> PBcR
-rwxrwxr-x 1 urbe urbe 131K Apr 10 11:41 PBcR
-rwxrwxr-x 1 urbe urbe 2,9M Apr 10 11:41 pbdagcon
-rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 pbutgcns
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 remove_fragment
-rwxrwxr-x 1 urbe urbe 153K Apr 10 11:40 removeMateOverlap
-rwxrwxr-x 1 urbe urbe 2,5K Apr 10 11:41 replaceUIDwithName-fastq
-rwxrwxr-x 1 urbe urbe 1,2K Apr 10 11:41 replaceUIDwithName-posmap
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 resolveSurrogates
-rwxrwxr-x 1 urbe urbe 139K Apr 10 11:41 rewriteCache
-rwxrwxr-x 1 urbe urbe 232K Apr 10 11:41 runCA
-rwxrwxr-x 1 urbe urbe  88K Apr 10 11:41 runCA-dedupe
-rwxrwxr-x 1 urbe urbe  14K Apr 10 11:41 runCA-overlapStoreBuild
-rwxrwxr-x 1 urbe urbe 3,6K Apr 10 11:41 run_greedy.csh
-rwxrwxr-x 1 urbe urbe 297K Apr 10 11:40 sffToCA
-rwxrwxr-x 1 urbe urbe  13K Apr 10 11:40 show-corrects
-rwxrwxr-x 1 urbe urbe 557K Apr 10 11:41 splitUnitigs
-rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 terminator
drwxrwxr-x 2 urbe urbe 4,0K Apr 10 11:41 TIGR
-rwxrwxr-x 1 urbe urbe 526K Apr 10 11:41 tigStore
-rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracearchiveToCA
-rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracedb-to-frg.pl
-rwxrwxr-x 1 urbe urbe  44K Apr 10 11:41 trimFastqByQVWindow
-rwxrwxr-x 1 urbe urbe  18K Apr 10 11:40 uidclient
-rwxrwxr-x 1 urbe urbe 589K Apr 10 11:41 unitigger
-rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v8-to-v9
-rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v9-to-v10
-rwxrwxr-x 1 urbe urbe  854 Apr 10 11:41 utg2fasta
-rwxrwxr-x 1 urbe urbe 731K Apr 10 11:41 utgcns
-rwxrwxr-x 1 urbe urbe 561K Apr 10 11:41 utgcnsfix


Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

]]> Jit https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data Wed, 17 Jan 2018 03:47:46 -0600 https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data <![CDATA[GPOPSIM: a simulation tool for whole-genome genetic data]]> GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

]]> Jit https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy Tue, 26 Nov 2013 17:34:00 -0600 https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy <![CDATA[DNA tale of 3 to 4 years old Serbia boy]]> The genome of a young boy found underground at Mal’ta near Lake Baikal of eastern Siberia around 24,000 years ago came out as close relative of Europeans and Native Indians.

Link:

http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0

 

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html

]]> Rahul Agarwal https://bioinformaticsonline.com/videolist/watch/4962/bionics-transhumanism-and-the-end-of-evolution-full-documentary Fri, 27 Sep 2013 11:54:17 -0500 https://bioinformaticsonline.com/videolist/watch/4962/bionics-transhumanism-and-the-end-of-evolution-full-documentary <![CDATA[Bionics, Transhumanism, and the end of Evolution (Full Documentary)]]> Bionics, Transhumanism, and the end of Evolution (Full Documentary) . 2013 This documentary and the rest of the documentaries presented relate to important times and figures in history, historic places and sites, archaeology, science, conspiracy theories, and education. The Topics of these video documentaries are varied and cover ancient history, Rome, Greece, Egypt, science, technology, nature, planet earth, the solar system, the universe, World wars, Battles, education, Biographies, television, archaeology, Illuminati, Area 51, serial killers, paranormal, supernatural, cults, government cover-ups, corruption, martial arts, space, aliens, ufos, conspiracy theories, Annunaki, Nibiru, Nephilim, satanic rituals, religion, strange phenomenon, origins of Mankind]]> https://bioinformaticsonline.com/videolist/watch/6232/the-cat-evolution-domestication-and-genome-10k Sun, 10 Nov 2013 14:33:56 -0600 https://bioinformaticsonline.com/videolist/watch/6232/the-cat-evolution-domestication-and-genome-10k <![CDATA[The Cat: evolution, domestication and Genome 10k]]> A public lecture by Dr Stephen J O'Brien at the UCD Earth Institute, University College Dublin, Ireland. Dr O'Brien is a world leading molecular biologist and dedicated conservationist who uses the tools of molecular biology to help protect endangered species and understand devastating diseases such as cancer and AIDS. He received his PhD in Genetics from Cornell University, USA, in 1971. He then joined the prestigious National Cancer Institute, National Institutes of Health as a post-doc in 1971 and, there, served as Founder and Chief of the Laboratory of Genomic Diversity from 1986-2011. In December 2011, he joined the Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, Russia, as Chief Scientific Officer. Convinced of the utility of exploring diverse species to advance our understanding of the human genome, Dr O'Brien and his team have assembled over 62,000 animal and 424,000 human tissue/DNA specimens, facilitating wide-ranging studies of disease gene associations, species adaptation and natural history. His research interests and expertise span human and comparative genomics, genetic epidemiology, HIV/AIDS, retro-virology, bioinformatics biodiversity and species conservation. Dr O'Brien is best known for documenting the remarkable genetic uniformity of African cheetahs, resolving the mammalian tree of life, describing heretofore unrecognized species of Orangutans, African forest elephants, and Bornean clouded leopards. He is credited with the discovery of CCR5 delta 32, the first of 20 human AIDS restriction genes, which imparts natural immunity to HIV. He is the one of the founders of the Genome 10K initiative, has published over 750 leading research papers, written multiple books and is adjunct professor in over 12 international leading universities. The UCD Earth Institute, University College Dublin, is a multidisciplinary research and education centre with a focus on creating and sharing new knowledge. We aim to contribute to sustainable solutions for many of the pressing Earth-related problems affecting societies now and in the near future.]]> https://bioinformaticsonline.com/researchlabs/view/11107/the-minerva-research-group-for-bioinformatics Tue, 27 May 2014 15:48:14 -0500 <![CDATA[The Minerva Research Group for Bioinformatics]]> The focus of the bioinformatics group is to use computational approaches to gain an insight into genome evolution in primates.

http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27

Kelso Group
Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Phone: +49 341 3550 500

Job:
http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40

]]> https://bioinformaticsonline.com/opportunity/view/13338/protein-function-annotation-and-machine-learning-upmc-paris-france Sat, 02 Aug 2014 01:22:52 -0500 <![CDATA[Protein function annotation and machine learning - UPMC - Paris, France]]> Protein function annotation and machine learning - UPMC - Paris, France

Job Description: We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.

We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.

Where: This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

Start date: September 1st, 2014
Contact Person: Alessandra Carbone
Contact: alessandra.carbone@lip6.fr

]]>