A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

Widespread false gene gains caused by duplication errors in genome assemblies

False duplications in genome assemblies lead to false biological conclusions. We quantified false duplications in previous genome assemblies and their new counterparts of the same species (platypus, zebra finch, Anna’s hummingbird) generated by the Vertebrate Genomes Project (VGP). Whole genome alignments revealed that 4 to 16% of the sequences were falsely duplicated in the previous assemblies, impacting hundreds to thousands of genes. These led to overestimated gene family expansions. The main source of the false duplications was heterotype duplications, where the haplotype sequences were more divergent than other parts of the genome leading the assembly algorithms to classify them as separate genes or genomic regions. A minor source was sequencing errors. Although present in a smaller proportion, we observed false duplications remaining in the VGP assemblies that can be identified and purged. This study highlights the need for more advanced assembly methods that better separates haplotypes and sequence errors, and the need for cautious analyses on gene gains.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled – one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.1 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200803 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.1/v1.3.1 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.3.0 was reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.7.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish, NextDenovo/NextPolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

Eutherian comparative genomic analysis protocol

The eutherian genomics momentum greatly advanced biological and medical sciences. Yet, future revisions and updates of eutherian genomic sequence data sets were expected, due to potential genomic sequence errors and incompleteness of genomic sequences. The eutherian comparative genomic analysis protocol was established as guidance in protection against potential genomic sequence errors in public eutherian genomic sequence assemblies. The protocol revised, updated and published 14 major eutherian gene data sets, including 2615 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets under accession numbers: FR734011-FR734074, HF564658-HF564785, HF564786-HF564815, HG328835-HG329089, HG426065-HG426183, HG931734-HG931849, LM644135-LM644234, LN874312-LN874522, LT548096-LT548244, LT631550-LT631670, LT962964-LT963174, LT990249-LT990597, LR130242-LR130508 and LR760818-LR761312.

Phylogeographic analysis of SARS-CoV-2 variations from Indian isolates

The aggressive multiplication of coronavirus in the Indian population is probably due to the faster mutation rate. At the time of commencement of this work, India was not present in the list of Top 10 worst-affected countries. However, upon completion of this manuscript, India is ranked No. 3 and during publication of this manuscript it may even elevate to the top two positions due to the pandemic. In this study, SARS-CoV-2 isolates of Indian origin were compared with the Wuhan reference sequence. Phylogenomic, proteomic, and phylogeographic analyses were performed. The genome comparisons indicated that majority of the sequence variations are associated with protein-coding regions, especially Orf1a and spike glycoproteins, while Orf7a had consistent variations, whereas Orfs 6a, 8 and 10 had negligible variations. The terminals of the genomes compared had high sequence entropy. However, the polyadenylation signal was invariant in the analysed dataset. The codon usage frequency indicated that UGU (code for cysteine) is the most frequent codon, while the least frequent was GCG (code for alanine). The amino acid frequency showed that the most abundant was leucine (12.5%), and the least was histidine (2.45%). The phylogeographical patterns were mapped for all the representative states of India, and were supplemented with few representative countries. The unique differences in the sequence of the Kerala isolate (EPI_ISL_413522) were resolved to be sequence errors rather than mutations. Based on the phylogeographic analysis, the high probability of mutations likely to be of Indian origin is attributed to the Gujarat cluster.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled – one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.0 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200119 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.0/v1.2.4 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.1.10 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.5.1 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

Long-read only assembly of Drechmeria coniospora genomes reveals widespread chromosome plasticity and illustrates the limitations of current nanopore methods

Background Long-read sequencing is increasingly being used to determine eukaryotic genomes. We used nanopore technology to generate chromosome-level assemblies for 3 different strains of Drechmeria coniospora, a nematophagous fungus used extensively in the study of innate immunity in Caenorhabditis elegans. Results One natural geographical isolate demonstrated high stability over decades, whereas a second isolate not only had a profoundly altered genome structure but exhibited extensive instability. We conducted an in-depth analysis of sequence errors within the 3 genomes and established that even with state-of-the-art tools, nanopore methods alone are insufficient to generate eukaryotic genome sequences of sufficient accuracy to merit inclusion in public databases. Conclusions Although nanopore long-read sequencing is not accurate enough to produce publishable eukaryotic genomes, in our case, it has revealed new information about genome plasticity in D. coniospora and provided a backbone that will permit future detailed study to characterize gene evolution in this important model fungal pathogen.

Eutherian comparative genomic analysis protocol

The eutherian genomics momentum greatly advanced biology and medicine. Nevertheless, future revisions and updates of eutherian genomic sequence data sets were expected, due to potential genomic sequence errors and incompleteness of genomic sequences. The eutherian comparative genomic analysis protocol was established as guidance in protection against potential genomic sequence errors in public eutherian genomic sequence assemblies. The protocol revised, updated and published 12 major eutherian gene data sets, including 1853 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets under accession numbers: FR734011-FR734074, HF564658-HF564785, HF564786-HF564815, HG328835-HG329089, HG426065-HG426183, HG931734-HG931849, LM644135-LM644234, LN874312-LN874522, LT548096-LT548244, LT631550-LT631670, LT962964-LT963174 and LT990249-LT990597.

Long-read only assembly of Drechmeria coniospora genomes reveals widespread chromosome plasticity and illustrates the limitations of current nanopore methods

Long read sequencing is increasingly being used to determine eukaryotic genomes. We used nanopore technology to generate chromosome-level assemblies for 3 different strains of Drechmeria coniospora, a nematophagous fungus used extensively in the study of innate immunity in Caenorhabditis elegans. One natural geographical isolate demonstrated high stability over decades, whereas a second isolate, not only had a profoundly altered genome structure, but exhibited extensive instability. We conducted an in-depth analysis of sequence errors within the 3 genomes and established that even with state-of-the-art tools, nanopore methods alone are insufficient to generate eukaryotic genome sequences of sufficient accuracy to merit inclusion in public databases.

NextPolish: a fast and efficient genome polishing tool for long-read assembly

Motivation Although long-read sequencing technologies can produce genomes with long contiguity, they suffer from high error rates. Thus, we developed NextPolish, a tool that efficiently corrects sequence errors in genomes assembled with long reads. This new tool consists of two interlinked modules that are designed to score and count K-mers from high quality short reads, and to polish genome assemblies containing large numbers of base errors. Results When evaluated for the speed and efficiency using human and a plant (Arabidopsis thaliana) genomes, NextPolish outperformed Pilon by correcting sequence errors faster, and with a higher correction accuracy. Availability and implementation NextPolish is implemented in C and Python. The source code is available from https://github.com/Nextomics/NextPolish. Supplementary information Supplementary data are available at Bioinformatics online.