Improving the usability and comprehensiveness of microbial databases

Loeffler, Caitlin; Karlsberg, Aaron; Martin, Lana S.; Eskin, Eleazar; Koslicki, David; Mangul, Serghei

doi:10.1186/s12915-020-0756-z

Cited by 18 publications

(14 citation statements)

References 9 publications

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“…The latter groups are assigned a unique digital object identifier (DOI) in the interim. Loeffler et al [ 80 ] discuss the importance of reference genomes to metagenomics studies but pertinently warn of the poor integration of new sequences into the existing database as well as the cooperative interactions between different databases with the resulting lack of a credibly comprehensive database despite progress in sequence curation. The direct implication is the doubtful identification of any fungi that are analysed using only one reference database [ 81 ].…”

Section: Advances In Molecular Characterisation Of Fungimentioning

confidence: 99%

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

Ijoma

Heri

Matambo

et al. 2021

JoF

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Identifying and adopting industrial applications for proteins and enzymes derived from fungi strains have been at the focal point of several studies in recent times. To facilitate such studies, it is necessary that advancements and innovation in mycological and molecular characterisation are concomitant. This review aims to provide a detailed overview of the necessary steps employed in both qualitative and quantitative research using the omics technologies that are pertinent to fungi characterisation. This stems from the understanding that data provided from the functional characterisation of fungi and their metabolites is important towards the techno-economic feasibility of large-scale production of biological products. The review further describes how the functional gaps left by genomics, internal transcribe spacer (ITS) regions are addressed by transcriptomics and the various techniques and platforms utilised, including quantitive reverse transcription polymerase chain reaction (RT-qPCR), hybridisation techniques, and RNA-seq, and the insights such data provide on the effect of environmental changes on fungal enzyme production from an expressional standpoint. The review also offers information on the many available bioinformatics tools of analysis necessary for the analysis of the overwhelming data synonymous with the omics approach to fungal characterisation.

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Section: Advances In Molecular Characterisation Of Fungimentioning

confidence: 99%

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

Ijoma

Heri

Matambo

et al. 2021

JoF

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“…These reads of low mapping quality are usually discarded. Furthermore, some species have no reliable reference, which is common in microbes [12].…”

Section: Introductionmentioning

confidence: 99%

2-kupl: mapping-free variant detection from DNA-seq data of matched samples

Wang

Xue

Pourcel

et al. 2021

Preprint

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The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Herein, we introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves a higher precision than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease.

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“…This is an important issue - because as new genome data is generated, training data sets, such as the commonly used NCBI Reference Sequence Database (RefSeq), will change over time. Consistency between databases and completeness of genomes is already an issue plaguing these databases [ 15 ]. Loeffler et al highlight the cost of computational power and storage requirements of maintaining a master reference sequence database, with a proposed solution of a “ continuous assembly approach” (supported by the institution’s infrastructure and the scientific community supplying the data sources) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Consistency between databases and completeness of genomes is already an issue plaguing these databases [ 15 ]. Loeffler et al highlight the cost of computational power and storage requirements of maintaining a master reference sequence database, with a proposed solution of a “ continuous assembly approach” (supported by the institution’s infrastructure and the scientific community supplying the data sources) [ 15 ]. From a classifier perspective, Nasko et al recently demonstrated that more reads are classified (as opposed to be assigned to an unclassified/unknown class) by the Kraken classifier with newer database versions [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

2020

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Background It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. Results We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss. Conclusions It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.

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Improving the usability and comprehensiveness of microbial databases

Cited by 18 publications

References 9 publications

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

2-kupl: mapping-free variant detection from DNA-seq data of matched samples

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

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