FragGeneScan: predicting genes in short and error-prone reads

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

BackgroundNext-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.ResultsWe conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others.ConclusionsWe developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0686-x) contains supplementary material, which is available to authorized users.

Whole genome sequencing provides the highest resolution for characterizing pathogen evolution, epidemiology, and diagnostics. Genome assemblies contain information on the identity and potential phenotypes of a pathogen. Likewise, variant calling can inform on transmission patterns and evolutionary relationships. Recent improvements in Oxford Nanopore long-read sequencing have made its use attractive for genomic epidemiology. However, the accuracy and optimal strategy for analysis of Nanopore reads remains to be determined. We compared the use of Illumina short reads and Oxford Nanopore long reads for genome assembly and variant calling of phytopathogenic bacteria. We generated short- and long-read datasets for diverse phytopathogenic Agrobacterium strains. We then analyzed these data using multiple pipelines designed for either short or long reads and compared the results. We found that assemblies made from long reads were more complete than those made from short-read data and contained few sequence errors. Variant calling pipelines differed in their ability to accurately call variants and infer genotypes from long reads. Results suggest that computationally fragmenting long reads can improve the accuracy of variant calling in population-level studies. Using fragmented long reads, pipelines designed for short reads were more accurate at recovering genotypes than pipelines designed for long reads. Further, short- and long-read datasets can be analyzed together with the same pipelines. These findings show that Oxford Nanopore sequencing is accurate and can be sufficient for microbial pathogen genomics and epidemiology. Ultimately, this enhances the ability of researchers and clinicians to understand and mitigate the spread of pathogens.

De novo DNA sequence assembly is very important in genome sequence analysis. In this paper, we investigated two of the major approaches for de novo DNA sequence assembly of very short reads: overlap-layout-consensus (OLC) and Eulerian path. From that investigation, we developed a new assembly technique by combining the OLC and the Eulerian path methods in a hierarchical process. The contigs yielded by these two approaches were treated as reads and were assembled again to yield longer contigs. We tested our approach using three real very-short-read datasets generated by an Illumina Genome Analyzer and four simulated very-short-read datasets that contained sequencing errors. The sequencing errors were modeled based on Illumina's sequencing technology. As a result, our combined approach yielded longer contigs than those of Edena (OLC) and Velvet (Eulerian path) in various coverage depths and was comparable to SOAPdenovo, in terms of N50 size and maximum contig lengths. The assembly results were also validated by comparing contigs that were produced by assemblers with their reference sequence from an NCBI database. The results show that our approach produces more accurate results than Velvet, Edena, and SOAPdenovo alone. This comparison indicates that our approach is a viable way to assemble very short reads from next generation sequencers.

Recent technological and computational advances have made metagenomic assembly a viable approach to achieving high-resolution views of complex microbial communities. In previous benchmarking, short-read (SR) metagenomic assemblers had the highest accuracy, long-read (LR) assemblers generated the most contiguous sequences and hybrid (HY) assemblers balanced length and accuracy. However, no assessments have specifically compared the performance of these assemblers on low-abundance species, which include clinically relevant organisms in the gut. We generated semi-synthetic LR and SR datasets by spiking small and increasing amounts of Escherichia coli isolate reads into fecal metagenomes and, using different assemblers, examined E. coli contigs and the presence of antibiotic resistance genes (ARGs). For ARG assembly, although SR assemblers recovered more ARGs with high accuracy, even at low coverages, LR assemblies allowed for the placement of ARGs within longer, E. coli-specific contigs, thus pinpointing their taxonomic origin. HY assemblies identified resistance genes with high accuracy and had lower contiguity than LR assemblies. Each assembler type's strengths were maintained even when our isolate was spiked in with a competing strain, which fragmented and reduced the accuracy of all assemblies. For strain characterization and determining gene context, LR assembly is optimal, while for base-accurate gene identification, SR assemblers outperform other options. HY assembly offers contiguity and base accuracy, but requires generating data on multiple platforms, and may suffer high misassembly rates when strain diversity exists. Our results highlight the trade-offs associated with each approach for recovering low-abundance taxa, and that the optimal approach is goal-dependent.

Large-scale population analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short-read whole-genome sequencing. However, these short-read approaches fail to give a complete picture of a genome. They struggle to identify structural events, cannot access repetitive regions, and fail to resolve the human genome into haplotypes. Here, we describe an approach that retains long range information while maintaining the advantages of short reads. Starting from ∼1 ng of high molecular weight DNA, we produce barcoded short-read libraries. Novel informatic approaches allow for the barcoded short reads to be associated with their original long molecules producing a novel data type known as “Linked-Reads”. This approach allows for simultaneous detection of small and large variants from a single library. In this manuscript, we show the advantages of Linked-Reads over standard short-read approaches for reference-based analysis. Linked-Reads allow mapping to 38 Mb of sequence not accessible to short reads, adding sequence in 423 difficult-to-sequence genes including disease-relevant genes STRC, SMN1, and SMN2. Both Linked-Read whole-genome and whole-exome sequencing identify complex structural variations, including balanced events and single exon deletions and duplications. Further, Linked-Reads extend the region of high-confidence calls by 68.9 Mb. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

Metagenomics enables direct investigation of the gene content and potential functions of gut bacteria without isolation and culture. However, metagenome-assembled genomes are often incomplete and have low contiguity due to challenges in assembling repeated genomic elements. Long-read sequencing approaches have successfully yielded circular bacterial genomes directly from metagenomes, but these approaches require high DNA input and can have high error rates. Illumina has recently launched the Illumina Complete Long Read (ICLR) assay, a new approach for generating kilobase-scale reads with low DNA input requirements and high accuracy. Here, we evaluate the performance of ICLR sequencing for gut metagenomics for the first time. We sequenced a microbial mock community and 10 human gut microbiome samples with standard, shotgun 2 × 150 paired-end sequencing, ICLR sequencing, and nanopore long-read sequencing and compared performance in read lengths, assembly contiguity, and bin quality. We find that ICLR human metagenomic assemblies have higher N50 (119.5 ± 24.8 kilobases) than short read assemblies (9.9 ± 4.5 kilobases; P = 0.002), and comparable N50 to nanopore assemblies (91.0 ± 43.8 kilobases; P = 0.32). Additionally, we find that ICLR draft microbial genomes are more complete (94.0% ± 20.6%) than nanopore draft genomes (85.9% ± 23.0%; P ≤ 0.001), and that nanopore draft genomes have truncated gene lengths (924.6 ± 114.7 base pairs) relative to ICLR genomes (954.6 ± 71.5 base pairs; P ≤ 0.001). Overall, we find that ICLR sequencing is a promising method for the accurate assembly of microbial genomes from gut metagenomes.IMPORTANCEMetagenomic sequencing allows scientists to directly measure the genome content and structure of microbes residing in complex microbial communities. Traditional short-read metagenomic sequencing methods often yield fragmented genomes, whereas advanced long-read sequencing methods improve genome assembly quality but often suffer from high error rates and are logistically limited due to high input requirements. A new method, the Illumina Complete Long Read (ICLR) assay, is capable of generating highly accurate kilobase-scale sequencing reads with minimal input material. To evaluate the utility of ICLR in metagenomic contexts, we applied short-read, long-read, and ICLR methods to simple and complex microbial communities. We found that ICLR outperforms short-read methods and yields comparable metagenomic assemblies to standard long-read approaches while requiring less input material. Overall, ICLR represents an additional option for assembling complete genomes from complex metagenomes.

### Widespread RNA and DNA Sequence Differences in the Human Transcriptome Li et al Science . 2011;333:53–58. New genomic studies show that the transfer of genetic information from DNA to RNA within humans is not very faithful. If true, frequent, or purposeful, then the implications for human health and disease are significant, but the burden of proof required is significant. Commentary: Molecular biology and genetics has a “central dogma.” First postulated by Francis Crick1,2 in 1958, shortly after the discovery of the double helix, it states that the molecular transfer or flow of genetic information occurs only as DNA→RNA→ protein. This argument is intrinsic to our understanding of genotypes, phenotypes, and human disease. The central “dogma,” a word whose precise meaning Crick acknowledged he did not precisely know3 and that could have been given the less glamorous moniker “hypothesis,” is frequently misunderstood as also implying high accuracy or even inflexibility.2 We now know that genetic information flow can be sometimes corrupted and can also occur in other directions in particular biological systems. A recent study by Li and colleagues,4 from the laboratory of Vivian Cheung at the University of Pennsylvania, now demonstrates apparent widespread DNA→RNA sequence differences in humans. The experiments they conducted involved sequencing reverse-transcribed RNA from cultured B cells isolated from 27 different humans and comparing them with their host genome sequences. Surprisingly, after appropriate quality controls, they discovered numerous base positions at which the RNA sequence was different from the DNA sequence in multiple molecules, a phenomenon they refer to as RDD (RNA-DNA difference). Specifically, they identified 28766 RDDs in the 27 individuals at 10210 exonic sites within 4741 genes. Each individual had high-quality RNA-based sequence data at 9.8 million sites: so, RDD was detected at a rate of 1.1×10−4 per exonic site (base) across human genes. Interestingly, they observed all 12 types …

Vitamin-A levels have been determined in healthy non-pregnant women and in women taking synthetic steroids to suppress ovulation. A significant increase has been found in those receiving oral contraceptives. Further work is necessary to determine whether this may affect the fetus in patients who become pregnant shortly after discontinuing oral contraception.

Motivation:

Traditional microarray approaches to gene expression profiling often require RNA or cDNA amplification when working with extremely small or valuable tissue samples.This process is generally viewed as being undesirable because there is potential for bias to be introduced during amplification. Very recently, the so-called next-generation sequencing technologies were adapted for use in global gene expression profiling. Herein we report our efforts to apply these sequencing technologies to assess relative transcript abundances in pre-implantation-stage porcine embryos, without additional nucleic acid amplification before sequencing. As a proof-of-principle experiment, we have isolated total RNA from the embryonic disc (inner cell mass; ICM) and a small piece of trophectoderm (TE) from a Day 12 in vivo-produced embryo, which were estimated to be composed of 500 to 1000 cells each. The RNA was reverse transcribed using oligo-dT priming followed by second-strand cDNA synthesis. The double-stranded cDNA was then randomly sheared by sonication, and 10 ng of double-stranded cDNA fragments was used for sample preparation before sequencing. Prepared cDNA fragments (at 7 picomolar concentrations) were submitted for sequencing using the Illumina/Solexa platform as recommended. The millions of short (36 bp) reads generated by Illumina sequencing for each sample were then aligned to the swine UniGene database from NCBI, allowing for zero or one mismatches. Relative transcript abundances between cell types were profiled by considering the read counts for a given UniGene member as a percentage of the total number of reads generated for each cell type. It was demonstrated that approximately 11 000 and 9000 UniGene members were represented by a normalized average of 5 or more short reads per lane (0.001% of the total) in the ICM and TE samples, respectively. As expected, pluripotency factors, chromatin remodeling components, and cell-cell communication molecules were overrepresented in the ICM sample as compared with the TE sample. Conversely, epithelial determinants, ion transporters, and components of the steroid biosynthesis pathways were more abundant in the TE sample than in the ICM sample. Relative abundances of representative transcripts in these samples were verified by quantitative RT-PCR. In conclusion, we demonstrate the utility of next-generation sequencing technologies for gene expression profiling using even minute tissue samples and show that such analyses are possible even in species without a sequenced genome.

In functional metagenomics, BLAST homology search is a common method to classify metagenomic reads into protein/domain sequence families such as Clusters of Orthologous Groups of proteins (COGs) in order to quantify the abundance of each COG in the community. The resulting functional profile of the community is then used in downstream analysis to correlate the change in abundance to environmental perturbation, clinical variation, and so on. However, the short read length coupled with next-generation sequencing technologies poses a barrier in this approach, essentially because similarity significance cannot be discerned by searching with short reads. Consequently, artificial functional families are produced, in which those with a large number of reads assigned decreases the accuracy of functional profile dramatically. There is no method available to address this problem. We intended to fill this gap in this paper. We revealed that BLAST similarity scores of homologues for short reads from COG protein members coding sequences are distributed differently from the scores of those derived elsewhere. We showed that, by choosing an appropriate score cut-off, we are able to filter out most artificial families and simultaneously to preserve sufficient information in order to build the functional profile. We also showed that, by incorporated application of BLAST and RPS-BLAST, some artificial families with large read counts can be further identified after the score cutoff filtration. Evaluated on three experimental metagenomic datasets with different coverages, we found that the proposed method is robust against read coverage and consistently outperforms the other E-value cutoff methods currently used in literatures.

The metagenomic paradigm allows for an understanding of the metabolic and functional potential of microbes in a community via a study of their proteins. The substrate for protein identification is either the set of individual nucleotide reads generated from metagenomic samples or the set of contig sequences produced by assembling these reads. However, a read-based strategy using reads generated by next-generation sequencing (NGS) technologies, results in an overwhelming majority of partial-length protein predictions. A nucleotide assembly-based strategy does not fare much better, as metagenomic assemblies are typically fragmented and also leave a large fraction of reads unassembled. Here, we present a method for reconstructing complete protein sequences directly from NGS metagenomic data. Our framework is based on a novel short peptide assembler (SPA) that assembles protein sequences from their constituent peptide fragments identified on short reads. The SPA algorithm is based on informed traversals of a de Bruijn graph, defined on an amino acid alphabet, to identify probable paths that correspond to proteins. Using large simulated and real metagenomic data sets, we show that our method outperforms the alternate approach of identifying genes on nucleotide sequence assemblies and generates longer protein sequences that can be more effectively analysed.

De novo assembly of metagenome samples is a common approach to the study of microbial communities. Current metagenome assemblers developed for short sequence reads or noisy long reads were not optimized for accurate long reads. We thus developed hifiasm-meta, a metagenome assembler that exploits the high accuracy of recent data. Evaluated on seven empirical datasets, hifiasm-meta reconstructed tens to hundreds of complete circular bacterial genomes per dataset, consistently outperforming other metagenome assemblers.

Recent advances in DNA sequencing technologies include the generation of long reads, with lengths from tens to hundreds of thousands of base pairs each. State-of-the-art algorithm Minimap2 is able to process these data and the most commonly used short reads, but is memory- and computationally-intensive: to process a human genome, its running times can reach up to several hours in powerful machines. As a means of making this technology more available to hospitals and clinics, hardware accelerators have addressed these shortcomings with many short read mappers in the past, and their application to this new generation of softwares is an area of active research. Here we present a FPGA-based accelerator for Minimap2 with focus on its operation for short reads. We gathered profiling behaviors to determine the algorithm's bottleneck. We generated a hardware block for one recurrent loop in the critical function that can be integrated into a parallelizable architecture. Execution with short reads has shown a reduction of 155x in terms of required clock cycles in the accelerated section. Data transfer overhead is measured and discussed.