Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis.

By Matthias Scholz, Chien-Chi Lo, Patrick S. G. Chain. 2012

361 citations recorded by the source corpus.

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Citations in the corpus

Clustering huge protein sequence sets in linear time (2017, background): “Therefore, computing and storage costs are now dominating metagenomics [4, 5, 6].”
MMseqs2: sensitive protein sequence searching for the analysis of massive data sets (2017, background): “In metagenomics, computational costs now dominate sequencing costs [5, 22, 27] and protein searches typically consume > 90% of computational resources [27], even though the gold 25”
The Potential of Metagenomic Approaches for Understanding Soil Microbial Processes (2014, background): “The result is that only a small fraction of soil metagenomic data can be assembled, usually into relatively small contigs (Table 1); however, a number of metagenome-specific assemblers have been developed in an attempt to overcome this hurdle (Scholz et al., 2012; Segata et al., 2013).”
Microbial Biodiversity and Bioremediation Assessment Through Omics Approaches (2020, background): “DNAmicroarrays have been reported to be used to assess the physiology and catabolic gene expression profile of microorganisms isolated from environmental samples (Schut et al., 2001; Dennis et al., 2003; Chandran et al., 2020).”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “It becomes more and more obvious that the bottleneck in laboratories has shifted from data generation to data management and interpretation [36].”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, background): “Homology-based or HMM strategies can deliver a great number of false negatives especially when using short reads (Scholz et al., 2012; Yang et al., 2014).”
CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database (2016, background): “With the advent of next-generation sequencing, enormous amounts of biological and clinical data are being generated throughout the biosciences (4) and AMR research is no exception.”
Assembling metagenomes, one community at a time (2017, background): “While numerous reviews on strategies to analyse metagenomic data have been published [51], there are currently no standard assembly procedures implemented to enable thorough comparative analyses between projects.”
SCycDB: A curated functional gene database for metagenomic profiling of sulphur cycling pathways (2020, background): “…S cycling genes (Baker et al., 2015; Vavourakis et al., 2019), difficulties in distinguishing homologous genes (e.g., sat vs. cysC, sbp vs. cysP, or psrA vs. phsA) (Marietou et al., 2018; Rückert, 2016; Wasmund et al., 2017), and long database searching time (Kim et al., 2013; Scholz et al., 2012).”