Sequence Effects on Block Copolymer Self-Assembly through Tuning Chain Conformation and Segregation Strength Utilizing Sequence-Defined Polypeptoids
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99 GENERAL AND MISCELLANEOUS↗
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Human RNome Project draft human RNome sequence of GM12878, B-cell line, obtained by mass-spectrometry sequencing, long-read sequencing and short-read sequencing.
Here we report the first draft of the human RNome sequence, a reference map of RNA chemical modifications in a human B-cell line. RNA carries a diverse repertoire of chemical modifications that regulate gene expression, cellular function, and responses to physiological and pathological cues. Yet, unlike the genome, no reference map of RNA modifications is available for any human cell. To generate this resource, the Human RNome Project Consortium analyzed a shared RNA preparation from the well-characterized GM12878 B-cell line using short-read sequencing, long-read direct RNA sequencing, and mass spectrometry, generating more than 7.1 billion sequencing reads spanning approximately 1.2 trillion nucleotides. The resulting maps of the human RNome reveal that RNA modifications are organized according to function, transcript architecture, and cellular identity. Modifications concentrate at functional centers of ribosomal and transfer RNAs, follow the canonical topology of N6-methyladenosine in coding transcripts, and form coordinated hotspots in immune regulatory genes. This first reference human RNome provides a foundation for understanding how RNA chemistry shapes cellular identity, human disease, and the development of RNA-based therapeutics.
59 BASIC BIOLOGICAL SCIENCES↗
Complete genome sequence of Pelosinus sp. strain UFO1 assembled using single-molecule real-time DNA sequencing technology
Pelosinus fermentans strain R7 was isolated from Russian kaolin clays as the type strain and it can reduce Fe(III) during fermentative growth (1). Draft genome sequences for P. fermentans R7 and four strains from Hanford, Washington, USA, have been published (2–4). The P. fermentans 16S rRNA sequence dominated the lactate-based enrichment cultures from three geochemically contrasting soils from the Melton Branch Watershed, Oak Ridge, Tennessee, USA (5) and also at another stimulated, uraniumcontaminated field site near Oak Ridge (6). For the current work, strain UFO1 was isolated from pristine sediments at a background field site in Oak Ridge and characterized as facilitating U(VI) reduction and precipitation with phosphate (7).
59 BASIC BIOLOGICAL SCIENCES↗
Saccharomycopsis sp. strain UFMG-CM-Y6632 internal transcribed spacer 1, partial sequence; 5.8S ribosomal RNA gene and internal transcribed spacer 2, complete sequence; and large subunit ribosomal RNA gene, partial sequence
Three yeast isolates were obtained from soil and rotting wood samples collected in an Amazonian rainforest biome in Brazil. Comparison of the intergenic spacer 5.8S region and the D1/D2 domains of the large subunit rRNA gene showed that the isolates represent a novel species of the genus Saccharomycopsis. A tree inferred from the D1/D2 sequences placed the novel species near a subclade containing Saccharomycopsis lassenensis, Saccharomycopsis fermentans, Saccharomycopsis javanensis, Saccharomycopsis babjevae, Saccharomycopsis schoenii and Saccharomycopsis oosterbeekiorum, but with low bootstrap support. In terms of sequence divergence, the novel species had the highest identity in the D1/D2 domains with Saccharomycopsis capsularis, from which it differed by 36 substitutions. In contrast, a phylogenomic analysis based on 1061 single-copy orthologs for a smaller set of Saccharomycopsis species whose whole genome sequences are available indicated that the novel species represented by strain UFMG-CM-Y6991 is phylogenetically closer to Saccharomycopsis fodiens and Saccharomycopsis sp. TF2021a (=Saccharomycopsis phalluae). The novel yeast is homothallic and produces asci with one spheroidal ascospore with an equatorial or subequatorial ledge. The name Saccharomycopsis praedatoria sp. nov. is proposed to accommodate the novel species. The holotype of Saccharomycopsis praedatoria is CBS 16589(T). The MycoBank number is MB849369. S. praedatoria was able to kill cells of Saccharomyces cerevisiae by means of penetration with infection pegs, a trait common to most species of Saccharomycopsis.
Amazonian Forest↗
Pichia sp. strain yHMH446 internal transcribed spacer 1, partial sequence; 5.8S ribosomal RNA gene, complete sequence; and internal transcribed spacer 2, partial sequence
A novel budding yeast species was isolated from a soil sample collected in the United States of America. Phylogenetic analyses of multiple loci and phylogenomic analyses conclusively placed the species within the genus Pichia. Strain yHMH446 falls within a clade that includes Pichia norvegensis, Pichia pseudocactophila, Candida inconspicua, and Pichia cactophila. Whole genome sequence data were analyzed for the presence of genes known to be important for carbon and nitrogen metabolism, and the phenotypic data from the novel species were compared to all Pichia species with publicly available genomes. Across the genus, including the novel species candidate, we found that the inability to use many carbon and nitrogen sources correlated with the absence of metabolic genes. Based on these results, Pichia galeolata sp. nov. is proposed to accommodate yHMH446(T) (=NRRL Y-64187 = CBS 16864). This study shows how integrated taxogenomic analysis can add mechanistic insight to species descriptions.
genome sequence↗
A method to determine fault vectors in 4H-SiC from stacking sequences observed on high resolution transmission electron microscopy images
A new method has been developed to determine the fault vectors associated with stacking faults in 4H-SiC from their stacking sequences observed on high resolution TEM images. This method, analogous to the Burgers circuit technique for determination of dislocation Burgers vector, involves determination of the vectors required in the projection of the perfect lattice to correct the deviated path constructed in the faulted material. Results for several different stacking faults were compared with fault vectors determined from X-ray topographic contrast analysis and were found to be consistent. This technique is expected to applicable to all structures comprising corner shared tetrahedra.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment
DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.
Simpson, Abigayle↗
Model morphing and sequence assignment after molecular replacement
A procedure for model building is described that combines morphing a model to match a density map, trimming the morphed model and aligning the model to a sequence. A procedure termed ‘morphing’ for improving a model after it has been placed in the crystallographic cell by molecular replacement has recently been developed. Morphing consists of applying a smooth deformation to a model to make it match an electron-density map more closely. Morphing does not change the identities of the residues in the chain, only their coordinates. Consequently, if the true structure differs from the working model by containing different residues, these differences cannot be corrected by morphing. Here, a procedure that helps to address this limitation is described. The goal of the procedure is to obtain a relatively complete model that has accurate main-chain atomic positions and residues that are correctly assigned to the sequence. Residues in a morphed model that do not match the electron-density map are removed. Each segment of the resulting trimmed morphed model is then assigned to the sequence of the molecule using information about the connectivity of the chains from the working model and from connections that can be identified from the electron-density map. The procedure was tested by application to a recently determined structure at a resolution of 3.2 Å and was found to increase the number of correctly identified residues in this structure from the 88 obtained using phenix.resolve sequence assignment alone (Terwilliger, 2003 ▶) to 247 of a possible 359. Additionally, the procedure was tested by application to a series of templates with sequence identities to a target structure ranging between 7 and 36%. The mean fraction of correctly identified residues in these cases was increased from 33% using phenix.resolve sequence assignment to 47% using the current procedure. The procedure is simple to apply and is available in the Phenix software package.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
in silico Whole Genome Sequencer & Analyzer (iWGS): A Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies
The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in nonmodel organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects, and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at https://github.com/zhouxiaofan1983/iWGS.
59 BASIC BIOLOGICAL SCIENCES↗
Genotyping‐in‐Thousands by sequencing (GT‐seq): A cost effective SNP genotyping method based on custom amplicon sequencing
Abstract Genotyping‐in‐Thousands by sequencing ( GT ‐seq) is a method that uses next‐generation sequencing of multiplexed PCR products to generate genotypes from relatively small panels (50–500) of targeted single‐nucleotide polymorphisms ( SNP s) for thousands of individuals in a single Illumina HiSeq lane. This method uses only unlabelled oligos and PCR master mix in two thermal cycling steps for amplification of targeted SNP loci. During this process, sequencing adapters and dual barcode sequence tags are incorporated into the amplicons enabling thousands of individuals to be pooled into a single sequencing library. Post sequencing, reads from individual samples are split into individual files using their unique combination of barcode sequences. Genotyping is performed with a simple perl script which counts amplicon‐specific sequences for each allele, and allele ratios are used to determine the genotypes. We demonstrate this technique by genotyping 2068 individual steelhead trout ( Oncorhynchus mykiss ) samples with a set of 192 SNP markers in a single library sequenced in a single Illumina HiSeq lane. Genotype data were 99.9% concordant to previously collected TaqMan ™ genotypes at the same 192 loci, but call rates were slightly lower with GT ‐seq (96.4%) relative to Taqman (99.0%). Of the 192 SNP s, 187 were genotyped in ≥90% of the individual samples and only 3 SNP s were genotyped in <70% of samples. This study demonstrates amplicon sequencing with GT ‐seq greatly reduces the cost of genotyping hundreds of targeted SNP s relative to existing methods by utilizing a simple library preparation method and massive efficiency of scale.
Campbell, Nathan R.↗
High-throughput sequencing of the chloroplast and mitochondrion of Chlamydomonas reinhardtii to generate improved de novo assemblies, analyze expression patterns and transcript speciation, and evaluate diversity among laboratory strains and wild isolates
Chlamydomonas reinhardtii is a unicellular chlorophyte alga that is widely studied as a reference organism for understanding photosynthesis, sensory and motile cilia, and for development of an algal-based platform for producing biofuels and bio-products. Its highly repetitive, ~205-kbp circular chloroplast genome and ~15.8-kbp linear mitochondrial genome were sequenced prior to the advent of high-throughput sequencing technologies. Here, high coverage shotgun sequencing was used to assemble both organellar genomes de novo. These new genomes correct dozens of errors in the prior genome sequences and annotations. Gen-ome sequencing coverage indicates that each cell contains on average 83 copies of the chloroplast genome and 130 copies of the mitochondrial genome. Using protocols and analyses optimized for organellar tran-scripts, RNA-Seq was used to quantify their relative abundances across 12 different growth conditions. Forty-six percent of total cellular mRNA is attributable to high expression from a few dozen chloroplast genes. RNA-Seq data were used to guide gene annotation, to demonstrate polycistronic gene expression, and to quantify splicing of psaA and psbA introns. In contrast to a conclusion from a recent study, we found that chloroplast transcripts are not edited. Unexpectedly, cytosine-rich polynucleotide tails were observed at the 3’-end of all mitochondrial transcripts. A comparative genomics analysis of eight laboratory strains and 11 wild isolates of C. reinhardtii identified 2658 variants in the organellargenomes, which is 1/10th as much genetic diversity as is found in the nucleus.
ftsH↗
Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry
PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).
Parris, D. Joshua↗
Next-Generation Sequencing Data from a CUT&RUN Study of R. toruloides IFO0880 Cse4 and Orc1 Binding Sites
Rhodotorula toruloides has been increasingly explored as a host for bioproduction of lipids, fatty acid derivatives and terpenoids. Various genetic tools have been developed, but neither a centromere nor an autonomously replicating sequence (ARS), both necessary elements for stable episomal plasmid maintenance, has yet been reported. In this study, cleavage under targets and release using nuclease (CUT&RUN), a method used for genome-wide mapping of DNA–protein interactions, was used to identify R. toruloides IFO0880 genomic regions associated with the centromeric histone H3 protein Cse4, a marker of centromeric DNA. Fifteen putative centromeres ranging from 8 to 19 kb in length were identified and analyzed, and four were tested for, but did not show, ARS activity. These centromeric sequences contained below average GC content, corresponded to transcriptional cold spots, were primarily nonrepetitive and shared some vestigial transposon-related sequences but otherwise did not show significant sequence conservation. Future efforts to identify an ARS in this yeast can utilize these centromeric DNA sequences to improve the stability of episomal plasmids derived from putative ARS elements.
Genome Engineering↗
The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success
Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.
54 ENVIRONMENTAL SCIENCES↗
CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments
In order to better define regions of similarity among related protein structures, it is useful to identify the residue-residue correspondences among proteins. Few codes exist for constructing a one-to-many multiple sequence alignment derived from a set of structure or sequence alignments, and a need was evident for creating such a tool for combining pairwise structure alignments that would allow for insertion of gaps in the reference structure. This report describes a new Python code, CombAlign, which takes as input a set of pairwise sequence alignments (which may be structure based) and generates a one-to-many, gapped, multiple structure- or sequence based sequence alignment (MSSA). The use and utility of CombAlign was demonstrated by generating gapped MSSAs using sets of pairwise structure-based sequence alignments between structure models of the matrix protein (VP40) and pre-small/secreted glycoprotein (sGP) of Reston Ebolavirus and the corresponding proteins of several other filoviruses. The gapped MSSAs revealed structure-based residue-residue correspondences, which enabled identification of structurally similar versus differing regions in the Reston proteins compared to each of the other corresponding proteins. CombAlign is a new Python code that generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA) given a set of pairwise sequence alignments (which may be structure based). CombAlign has utility in assisting the user in distinguishing structurally conserved versus divergent regions on a reference protein structure relative to other closely related proteins. CombAlign was developed in Python 2.6, and the source code is available for download from the GitHub code repository.
59 BASIC BIOLOGICAL SCIENCES↗
Sequencing at sea: challenges and experiences in Ion Torrent PGM sequencing during the 2013 Southern Line Islands Research Expedition
Genomics and metagenomics have revolutionized our understanding of marine microbial ecology and the importance of microbes in global geochemical cycles. However, the process of DNA sequencing has always been an abstract extension of the research expedition, completed once the samples were returned to the laboratory. During the 2013 Southern Line Islands Research Expedition, we started the first effort to bring next generation sequencing to some of the most remote locations on our planet. We successfully sequenced twenty six marine microbial genomes, and two marine microbial metagenomes using the Ion Torrent PGM platform on the Merchant Yacht Hanse Explorer. Onboard sequence assembly, annotation, and analysis enabled us to investigate the role of the microbes in the coral reef ecology of these islands and atolls. This analysis identified phosphonate as an important phosphorous source for microbes growing in the Line Islands and reinforced the importance of Lserine in marine microbial ecosystems. Sequencing in the field allowed us to propose hypotheses and conduct experiments and further sampling based on the sequences generated. By eliminating the delay between sampling and sequencing, we enhanced the productivity of the research expedition. By overcoming the hurdles associated with sequencing on a boat in the middle of the Pacific Ocean we proved the flexibility of the sequencing, annotation, and analysis pipelines.
59 BASIC BIOLOGICAL SCIENCES↗
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)
In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variablelength protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw amino acids k-mer features.
59 BASIC BIOLOGICAL SCIENCES↗
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)
ABSTRACT In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variable-length protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw k-mer features. Availability Implementations of our method will be available under the Apache 2 licence at http://llp.berkeley.edu/dimotif and http://llp.berkeley.edu/protvecx .
Asgari, Ehsaneddin↗