<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/3918?offset=180 https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads Tue, 02 Apr 2019 21:54:55 -0500 https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads <![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]> Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.

Address of the bookmark: https://github.com/fenderglass/Flye

]]> BioJoker https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization Fri, 24 Jan 2020 04:09:15 -0600 https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization <![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]> MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

]]> Jit https://bioinformaticsonline.com/researchlabs/view/1124/rolf-backofen-lab Thu, 18 Jul 2013 13:51:23 -0500 <![CDATA[Rolf Backofen Lab]]> The research interest of this group include constraint programming, structure prediction in simplified protein models, investigation of protein energy landscapes, detection of RNA sequence/structure motifs, prediction and evaluation of alternative splice forms, description and detection of regulatory sequences.

Link @ http://www.bioinf.uni-freiburg.de/

]]> 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/researchlabs/view/25993/hoffman-lab Tue, 12 Jan 2016 02:47:41 -0600 <![CDATA[Hoffman Lab]]> They develop machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight.

https://www.pmgenomics.ca/hoffmanlab/

]]> https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads Fri, 07 Sep 2018 05:19:06 -0500 https://bioinformaticsonline.com/bookmarks/view/37650/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads <![CDATA[P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads]]> P_RNA_scaffolder is a novel scaffolding tool using Pair-end RNA-seq to scaffold genome fragments. The method is suitable for most genomes. The program could utilize Illumina Paired-end RNA-sequencing reads from target speciesies. Our method provides another practical alternative to existing mate-pair_based approaches or other Protein-based approaches (for instance, PEP_scaffolder ) for scaffolding genome sequences. The most important feature of this method is to improve the completeness of gene regions and long-coding gene regions (for instance, circRNA).

Address of the bookmark: http://www.fishbrowser.org/software/P_RNA_scaffolder/#

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/41881/hdock-server Tue, 16 Jun 2020 01:54:41 -0500 https://bioinformaticsonline.com/bookmarks/view/41881/hdock-server <![CDATA[HDOCK SERVER]]> HDOCK SERVER

Protein-protein and protein-DNA/RNA docking based on a hybrid algorithm of template-based modeling and ab initio free docking.

The HDOCK server distinguishes itself from similar docking servers in its ability to support amino acid sequences as input and a hybrid docking strategy in which experimental information about the protein–protein binding site and small-angle X-ray scattering can be incorporated during the docking and post-docking processes.

Address of the bookmark: http://hdock.phys.hust.edu.cn/

]]> Neel 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 https://bioinformaticsonline.com/bookmarks/view/38053/swgis-v20-a-seqword-genomic-island-sniffer Thu, 01 Nov 2018 12:35:52 -0500 https://bioinformaticsonline.com/bookmarks/view/38053/swgis-v20-a-seqword-genomic-island-sniffer <![CDATA[swgis v2.0 : a seqword genomic island sniffer]]> swgis v2.0 is the modified version of the seqword genomic island sniffer. this version is specifically optimized for predicting genomic islands in eukaryotic genomes. swgis v2.0 was tested on several eukaryotic species of different lineages. all identified genomic islands were deposited in the eugi database.

download swgis v2.0

Address of the bookmark: http://eugi.bi.up.ac.za/eugi_download_swgis.php

]]> Abhimanyu Singh https://bioinformaticsonline.com/videolist/watch/22420/httpsyoutubecomdevicesupport Thu, 28 May 2015 07:39:19 -0500 https://bioinformaticsonline.com/videolist/watch/22420/httpsyoutubecomdevicesupport <![CDATA[https://youtube.com/devicesupport]]> https://youtube.com/devicesupport http://m.youtube.com]]>