scholarly journals SprayNPray: user-friendly taxonomic profiling of genome and metagenome contigs

2021 ◽  
Author(s):  
Arkadiy I Garber ◽  
Catherine R Armbruster ◽  
Stella E Lee ◽  
Vaughn S Cooper ◽  
Jennifer M Bomberger ◽  
...  
Keyword(s):  
Gc Content ◽  
Taxonomic Classification ◽  
Reference Database ◽  
Taxonomic Profiling ◽  
Spot Check ◽  
Domains Of Life ◽  
Multiple Domains ◽  
Multiple Metrics ◽  
User Friendly

Shotgun sequencing of cultured microbial isolates/individual eukaryotes (whole-genome sequencing) and microbial communities (metagenomics) has become commonplace in biology. Very often, sequenced samples encompass organisms spanning multiple domains of life, necessitating increasingly elaborate software for accurate taxonomic classification of assembled sequences. While many software tools for taxonomic classification exist, SprayNPray offers a quick and user-friendly, semi- automated approach, allowing users to separate contigs by taxonomy (and other metrics) of interest. Easy installation, usage, and intuitive output, which is amenable to visual inspection and/or further computational parsing, will reduce barriers for biologists beginning to analyze genomes and metagenomes. This approach can be used for broad-level overviews, preliminary analyses, or as a supplement to other taxonomic classification or binning software. SprayNPray profiles contigs using multiple metrics, including closest homologs from a user-specified reference database, gene density, read coverage, GC content, tetranucleotide frequency, and codon-usage bias. The output from this software is designed to allow users to spot-check metagenome-assembled genomes, identify, and remove contigs from putative contaminants in isolate assemblies, identify bacteria in eukaryotic assemblies (and vice-versa), and identify possible horizontal gene transfer events.

scholarly journals Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
F. A. Bastiaan von Meijenfeldt ◽  
Ksenia Arkhipova ◽  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Dna Sequences ◽  
De Novo ◽  
Taxonomic Classification ◽  
Classification Method ◽  
Reference Database ◽  
Annotation Tool ◽  
Multiple Signals

Abstract Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classifications are automatically made at low taxonomic ranks if closely related organisms are present in the reference database and at higher ranks otherwise. The result is a high classification precision even for sequences from considerably unknown organisms.

scholarly journals Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

2019 ◽  
Author(s):  
F.A. Bastiaan von Meijenfeldt ◽  
Ksenia Arkhipova ◽  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Dna Sequences ◽  
Real World ◽  
Taxonomic Classification ◽  
Reference Database ◽  
Annotation Tool ◽  
High Quality

ABSTRACTCurrent-day metagenomics increasingly requires taxonomic classification of long DNA sequences and metagenome-assembled genomes (MAGs) of unknown microorganisms. We show that the standard best-hit approach often leads to classifications that are too specific. We present tools to classify high-quality metagenomic contigs (Contig Annotation Tool, CAT) and MAGs (Bin Annotation Tool, BAT) and thoroughly benchmark them with simulated metagenomic sequences that are classified against a reference database where related sequences are increasingly removed, thereby simulating increasingly unknown queries. We find that the query sequences are correctly classified at low taxonomic ranks if closely related organisms are present in the reference database, while classifications are made higher in the taxonomy when closely related organisms are absent, thus avoiding spurious classification specificity. In a real-world challenge, we apply BAT to over 900 MAGs from a recent rumen metagenomics study and classified 97% consistently with prior phylogeny-based classifications, but in a fully automated fashion.

scholarly journals phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes

2019 ◽  
Author(s):  
Harald R. Gruber-Vodicka ◽  
Brandon K. B. Seah ◽  
Elmar Pruesse
Keyword(s):  
Molecular Ecology ◽  
Genomic Data ◽  
General Purpose ◽  
Reference Database ◽  
Rrna Gene ◽  
Ssu Rrna ◽  
Surface Ocean ◽  
Domains Of Life ◽  
User Friendly ◽  
Environmental Microbes

ABSTRACTThe SSU rRNA gene is the key marker in molecular ecology for all domains of life, but is largely absent from metagenome-assembled genomes that often are the only resource available for environmental microbes. Here we present phyloFlash, a pipeline to overcome this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based binning of full metagenomic assemblies. We show that a cleanup of artifacts is pivotal even with a curated reference database. With such a filtered database, the general-purpose mapper BBmap extracts SSU rRNA reads five times faster than the rRNA-specialized tool SortMeRNA with similar sensitivity and higher selectivity on simulated metagenomes. Reference-based targeted assemblers yielded either highly fragmented assemblies or high levels of chimerism, so we employ the general-purpose genomic assembler SPAdes. Our optimized implementation is independent of reference database composition and has satisfactory levels of chimera formation. Using the phyloFlash workflow we could recover the first complete genomes of several enigmatic taxa, including Marinamargulisbacteria from surface ocean seawater. phyloFlash quickly processes Illumina (meta)genomic data, is straightforward to use, even as part of high-throughput quality control, and has user-friendly output reports. The software is available at https://github.com/HRGV/phyloFlash (GPL3 license) and is documented with an online manual.

scholarly journals Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel P. Dacey ◽  
Frédéric J. J. Chain
Keyword(s):  
Read Depth ◽  
Taxonomic Composition ◽  
Taxonomic Classification ◽  
Read Length ◽  
Reference Database ◽  
Reference Databases ◽  
Sequence Quality ◽  
First Time ◽  
Mock Communities

Abstract Background Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases. Results The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes. Conclusions Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

PlncRNADB: A Repository of Plant lncRNAs and lncRNA-RBP Protein Interactions

2019 ◽  
Vol 14 (7) ◽  
pp. 621-627 ◽  
Author(s):  
Youhuang Bai ◽  
Xiaozhuan Dai ◽  
Tiantian Ye ◽  
Peijing Zhang ◽  
Xu Yan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Populus Trichocarpa ◽  
Noncoding Rnas ◽  
Reference Database ◽  
Protein Coding ◽  
Arabidopsis Lyrata ◽  
User Friendly

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

scholarly journals Captivating Perplexities of Spinareovirinae 5′ RNA Caps

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 294
Author(s):  
Justine Kniert ◽  
Qi Feng Lin ◽  
Maya Shmulevitz
Keyword(s):  
Immune Recognition ◽  
Domains Of Life ◽  
The Many ◽  
Rna Capping ◽  
Multiple Domains ◽  
Rna Caps

RNAs with methylated cap structures are present throughout multiple domains of life. Given that cap structures play a myriad of important roles beyond translation, such as stability and immune recognition, it is not surprising that viruses have adopted RNA capping processes for their own benefit throughout co-evolution with their hosts. In fact, that RNAs are capped was first discovered in a member of the Spinareovirinae family, Cypovirus, before these findings were translated to other domains of life. This review revisits long-past knowledge and recent studies on RNA capping among members of Spinareovirinae to help elucidate the perplex processes of RNA capping and functions of RNA cap structures during Spinareovirinae infection. The review brings to light the many uncertainties that remain about the precise capping status, enzymes that facilitate specific steps of capping, and the functions of RNA caps during Spinareovirinae replication.

Genetic characteristics and taxonomic classification of Fimic Anthrosols in China

Geoderma ◽  
2003 ◽  
Vol 115 (1-2) ◽  
pp. 31-44 ◽  
Author(s):  
Min Zhang ◽  
Li Ma ◽  
Wenqing Li ◽  
Baocheng Chen ◽  
Jiwen Jia
Keyword(s):  
Taxonomic Classification ◽  
Genetic Characteristics

Taxonomic classification of metagenomic sequences from Relative Abundance Index profiles using deep learning

2021 ◽  
Vol 67 ◽  
pp. 102539
Author(s):  
Meryem Altın Karagöz ◽  
O. Ufuk Nalbantoglu
Keyword(s):  
Deep Learning ◽  
Relative Abundance ◽  
Taxonomic Classification ◽  
Abundance Index

scholarly journals A singular value decomposition approach for improved taxonomic classification of biological sequences

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S11 ◽  
Author(s):  
Anderson R Santos ◽  
Marcos A Santos ◽  
Jan Baumbach ◽  
John A McCulloch ◽  
Guilherme C Oliveira ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Taxonomic Classification ◽  
Biological Sequences ◽  
Decomposition Approach ◽  
Value Decomposition
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