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Metagenomics data
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Screening and expression of new β-glucosidase genes (bgc) have attracted much attention because of their valuable application in a wide range of industrial areas such as bioethanol production, food and animal feed processing, paper making, and biotechnological processes.
10p
vicaptainmarvel
21-04-2023
2
2
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Glycoside Hydrolase family 5 (GH5) members share a broad range of enzymatic activities on oligosaccharides, polysaccharides and glycoconjugates from a wide range of species. The subfamily 4 (GH5-4) is enriched in some broad-specificity endo-β-1,4-endoglucanases (EG).
10p
vicaptainmarvel
21-04-2023
2
2
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In a previous study, a gene GL0694641 coding for endoglucanase containing 3 domains GH5- CBM72-CBM72 was exploited from metagenomic DNA data of bacteria in Vietnamese goats’ rumen. The gene (eg5) encoding the mature enzyme (without signal peptide coding sequence) was optimized codons, artificially synthesized, and inserted into the pET22b(+) vector at NcoI and XhoI to generate expression vector pET22-eg5 for expression of the gene in Eschrichia coli.
11p
vicaptainmarvel
21-04-2023
5
2
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Cellobiohydrolase (EC 3.2.1.91) is one of the important enzymes involved in cellulose hydrolysis. In this study, the gene sequences encoding cellobiohydrolase were extracted from the metagenome DNA data of microorganisms surrounding white-rot fungi in Cuc Phuong National Park based on the KEGG database. 73 ORFs encoding cellobiohydrolase were obtained, of which 15 ORFs contained complete genes, 6 ORFs with functional regions.
8p
vineville
08-02-2023
5
1
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Reconstructing genomic segments from metagenomics data is a highly complex task. In addition to general challenges, such as repeats and sequencing errors, metagenomic assembly needs to tolerate the uneven depth of coverage among organisms in a community and differences between nearly identical strains.
14p
vielonmusk
30-01-2022
13
0
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Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold.
13p
vielonmusk
30-01-2022
9
0
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One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited.
19p
vialfrednobel
29-01-2022
13
0
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Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data.
21p
vialfrednobel
29-01-2022
9
0
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Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated.
14p
viaristotle
29-01-2022
7
0
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The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.
31p
viarchimedes
26-01-2022
11
2
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Microbiome meta-analysis and crossdisease comparison enabled by the SIAMCAT machine learning toolbox
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues.
27p
viarchimedes
26-01-2022
14
0
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Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited in their ability to directly compare microbial metabolic function across samples and studies.
18p
viarchimedes
26-01-2022
7
0
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There is an increasing demand for accurate and fast metagenome classifiers that can not only identify bacteria, but all members of a microbial community. We used a recently developed concept in read mapping to develop a highly accurate metagenomic classification pipeline named CCMetagen.
15p
viarchimedes
26-01-2022
10
0
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African populations provide a unique opportunity to interrogate hostmicrobe co-evolution and its impact on adaptive phenotypes due to their genomic, phenotypic, and cultural diversity. We integrate gut microbiome 16S rRNA amplicon and shotgun metagenomic sequence data with quantification of pathogen burden and measures of immune parameters for 575 ethnically diverse Africans from Cameroon.
32p
viarchimedes
26-01-2022
8
0
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Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome.
16p
viarchimedes
26-01-2022
6
0
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Expression screening of environmental DNA (eDNA) libraries is a popular approach for the identification and characterization of novel microbial enzymes with promising biotechnological properties. In such “functional metagenomics” experiments, inserts, selected on the basis of activity assays, are sequenced with high throughput sequencing technologies. Assembly is followed by gene prediction, annotation and identification of candidate genes that are subsequently evaluated for biotechnological applications.
10p
vilarryellison
29-10-2021
9
1
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Exploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process. Although tools for this purpose exist for 16S ribosomal RNA sequencing analysis, there is a growing but still insufficient number of user-friendly interactive visualization workflows for easy data exploration and figure generation. The development of such platforms for this purpose is necessary to accelerate and streamline microbiome laboratory research.
11p
vibeauty
23-10-2021
10
1
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In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis.
17p
vibeauty
23-10-2021
10
2
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Modern metagenomic analysis of complex microbial communities produces large amounts of sequence data containing information on the microbiome in terms of bacterial, archaeal, viral and eukaryotic composition. The bioinformatics tools available are mainly devoted to profiling the bacterial and viral fractions and only a few software packages consider fungi.
7p
visilicon2711
20-08-2021
10
1
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Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community.
9p
visilicon2711
20-08-2021
11
1
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