<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36730?offset=160 https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins Sat, 06 Jul 2024 04:29:16 -0500 https://bioinformaticsonline.com/bookmarks/view/44595/squeezemeta-a-fully-automated-metagenomics-pipeline-from-reads-to-bins <![CDATA[SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins]]> SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:

  1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
  2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
  3. Includes binning and bin checking, for retrieving individual genomes
  4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
  5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
  6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus Fri, 05 Jan 2018 04:12:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus <![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]> proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

]]> Jit https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system Mon, 19 Aug 2013 15:24:26 -0500 https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system <![CDATA[What Junk DNA? It’s an Operating System]]> The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as “junk DNA,” is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/

]]> Rahul Agarwal https://bioinformaticsonline.com/researchlabs/view/6560/the-graveley-lab Tue, 19 Nov 2013 18:02:48 -0600 <![CDATA[The Graveley Lab]]> Research in the Graveley lab is primarily focused on the regulation of alternative splicing and small RNA mediated gene regulation. These are fascinating and extraordinarily important mechanisms by which genes can be regulated. Our long-term goals are to understand how these processes are regulated at a mechanistic level and to understand the logic of these processes in significant biological settings. To achieve these goals, we strive to think outside the box to creatively attack the problems being addressed using a wide variety of approaches that include biochemistry, genetics, imaging, deep sequencing, large-scale RNAi screening and bioinformatics.

Lab page @ http://graveleylab.cam.uchc.edu/Graveley/index.html

]]> https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid Sun, 26 Oct 2014 07:45:58 -0500 https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid <![CDATA[Genetic code - Amino Acid]]> The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

The image summarise all in one.

More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics Tue, 04 Oct 2016 14:50:48 -0500 https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics <![CDATA[MIRO : miRNA omics]]> The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.

Address of the bookmark: http://seq.crg.es/download/software/Miro/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome Tue, 26 Dec 2017 22:23:39 -0600 https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome <![CDATA[Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.]]> Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing Sun, 16 Feb 2020 08:47:17 -0600 https://bioinformaticsonline.com/bookmarks/view/41046/iseqqc-a-tool-for-expression-based-quality-control-in-rna-sequencing <![CDATA[iSeqQC: a tool for expression-based quality control in RNA sequencing]]> iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/42033/seastar-systematic-evaluation-of-alternative-start-site-in-rna Thu, 13 Aug 2020 09:54:27 -0500 https://bioinformaticsonline.com/bookmarks/view/42033/seastar-systematic-evaluation-of-alternative-start-site-in-rna <![CDATA[SEASTAR: Systematic Evaluation of Alternative STArt site in RNA]]> SEASTAR (Systematic Evaluation of Alternative STArt site in RNA) is a software package for Transcription Start Site (TSS) identification and quantification using only RNA-seq data. It assembles novel TSSs based only on RNA-Seq data and merges them with known TSSs from a public database. This package enables high-quality TSS identification that is comparable to the highly sophisticated CAGE technology. This package is particularly useful for finding novel TSSs that contribute to transcriptome complexity along with identifying differential promoter utilization.

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

]]> BioStar https://bioinformaticsonline.com/blog/view/44626/meta-transcriptomics-dynamic-world-of-rna-in-diverse-environments Wed, 31 Jul 2024 02:40:49 -0500 https://bioinformaticsonline.com/blog/view/44626/meta-transcriptomics-dynamic-world-of-rna-in-diverse-environments <![CDATA[Meta-Transcriptomics: Dynamic World of RNA in Diverse Environments]]> Meta-transcriptomics combines high-throughput sequencing technologies with computational biology to profile the RNA content of a sample. This technique allows researchers to capture a snapshot of gene expression and metabolic activities across diverse microbial communities, such as those found in soil, water, and the human gut.

Key Components

  1. Sample Collection: Meta-transcriptomics begins with the collection of environmental samples. These samples are often complex, containing a wide range of microorganisms.

  2. RNA Extraction: RNA is extracted from the sample, which includes mRNA, rRNA, tRNA, and other non-coding RNAs. This step is crucial as it determines the quality and representativeness of the data.

  3. Sequencing: High-throughput RNA sequencing (RNA-seq) technologies are used to obtain sequences of the RNA transcripts. This step provides a vast amount of data on the RNA molecules present in the sample.

  4. Data Analysis: Computational tools and bioinformatics methods are employed to process and analyze the sequencing data. This involves mapping RNA sequences to reference genomes or transcriptomes, identifying expressed genes, and quantifying their abundance.

  5. Functional Annotation: The functional roles of identified transcripts are inferred based on known gene functions, allowing researchers to understand the metabolic and ecological functions of the microbial community.

Applications

  1. Environmental Monitoring: Meta-transcriptomics can be used to monitor the health and functional status of ecosystems. For example, it can help assess the impact of pollution on microbial communities by revealing changes in gene expression related to stress response and degradation processes.

  2. Microbiome Research: In human health, meta-transcriptomics offers insights into the gut microbiome’s functional state. It helps in understanding how microbial communities interact with their host, how they respond to dietary changes, and their role in health and disease.

  3. Biotechnology: The technique can aid in the discovery of novel enzymes and bioactive compounds by profiling microbial communities in extreme environments or industrial processes.

  4. Disease Pathogenesis: By analyzing RNA profiles from disease-associated environments, researchers can uncover pathogen-host interactions and identify potential targets for therapeutic interventions.

Challenges

  1. Complexity of Data: The sheer volume and complexity of data generated by meta-transcriptomics can be overwhelming. Effective data management and advanced computational tools are required to extract meaningful insights.

  2. Sampling Bias: Environmental samples can be heterogeneous, and RNA extraction methods may introduce biases, potentially affecting the accuracy of the results.

  3. Reference Databases: Incomplete or biased reference databases can hinder the accurate functional annotation of transcripts, especially when studying novel or poorly characterized organisms.

Future Directions

Meta-transcriptomics is a rapidly evolving field, with ongoing advancements in sequencing technologies and bioinformatics. Future research may focus on improving data integration, developing more comprehensive reference databases, and enhancing our understanding of microbial community dynamics in various environments. As these challenges are addressed, meta-transcriptomics will continue to provide valuable insights into the functional roles of microorganisms and their interactions within ecosystems.

Conclusion

Meta-transcriptomics represents a powerful tool for exploring the functional aspects of microbial communities in their natural environments. By capturing a snapshot of gene expression and metabolic activities, this approach offers a deeper understanding of ecological interactions, health implications, and biotechnological potentials. As technology and methodologies advance, meta-transcriptomics is poised to make significant contributions to our knowledge of the microbial world.

]]> Abhi