Large Sequence Data Research Articles

DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.

Opportunities and challenges for the use of common controls in sequencing studies.

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Data Set-Adaptive Minimizer Order Reduces Memory Usage in k-Mer Counting.

The rapid continuous growth of deep sequencing experiments requires development and improvement of many bioinformatic applications for analysis of large sequencing data sets, including k-mer counting and assembly. Several applications reduce memory usage by binning sequences. Binning is done by using minimizer schemes, which rely on a specific order of the minimizers. It has been demonstrated that the choice of the order has a major impact on the performance of the applications. Here we introduce a method for tailoring the order to the data set. Our method repeatedly samples the data set and modifies the order so as to flatten the k-mer load distribution across minimizers. We integrated our method into Gerbil, a state-of-the-art memory-efficient k-mer counter, and were able to reduce its memory footprint by 30%-50% for large k, with only a minor increase in runtime. Our tests also showed that the orders produced by our method produced superior results when transferred across data sets from the same species, with little or no order change. This enables memory reduction with essentially no increase in runtime.

193 Arachnoid Cysts as Genetically-encoded Harbingers of Neurodevelopmental Pathology

INTRODUCTION: Arachnoid cysts (ACs) are the most common congenital intracranial lesions. While familial cases implicate genetic factors, their ultimate cause remains unknown. Herein, we describe a multi-omics approach to disease state characterization including traditional whole exome sequencing, integrative genomics with analytics of large bulk-RNA and single cell RNA sequencing (scRNAseq) data, and a novel phenomics technique using natural language processing and Human Phenotypic Ontological (HPO) language clustering. METHODS: Whole exome sequencing was performed on 1560 individuals (520 parent-offspring "trios"). Genes with likely pathogenic mutations were defined as those with >1 de novo variant (DNV) reaching threshold for exome-wide significance (p<2.5 x 10-6) and having high pLI score (>0.9). This high-confidence pathogenic gene set and a secondary probable pathogenic gene set (>1 DNV, regardless of exome-wide significance or pLI score) were analyzed in bulk-RNA and scRNAseq datasets to assess spatiotemporal and cell-type relationships of gene expression. Phenomic analysis was conducted by subjecting patient medical records to a natural-language-processing (NLP) algorithm. Phenotypes were catalogued with the Human Phenotype Ontology (HPO) language tree and mapped with uniform manifold approximation and projection (UMAP) clustering. RESULTS: 7 genes reached our pathogenic threshold, ADNP, ARIDB1, KDM5C, PURA, FOXP1, MAP2K1, and SCN2A. Strikingly, all but SCN2A play critical roles in epigenetic regulation of gene expression in the developing brain. Bulk-RNA datasets reveal that these genes converge in networks essential for chromatin remodeling and mRNA processing. scRNAseq datasets revealed an association with excitatory and inter-neurons known to play a role in developmental delay. HPO clustering revealed four major phenotypic clusters defined by neurodevelopmental delay (NDD), seizures, both or asymptomatic. In a subgroup of traditionally phenotyped patients, there was statistically significant enrichment of neuraxial phenotypes - including NDD - among those harboring DNVs. CONCLUSION: This novel association between ACs and multiple epigenetic regulators of gene expression provides the first insight into intracranial AC pathobiology. HPO clustering reveals clinically relevant subsets of ACs and implies that traditionally symptomatic or asymptomatic ACs may be harbingers of genetically-driven aberrant neurodevelopment.

Identification of pharmacogenetic variants from large scale next generation sequencing data in the Saudi population.

It is well documented that drug responses are related to Absorption, Distribution, Metabolism, and Excretion (ADME) characteristics of individual patients. Several studies have identified genetic variability in pharmacogenes, that are either directly responsible for or are associated with ADME, giving rise to individualized treatments. Our objective was to provide a comprehensive overview of pharmacogenetic variation in the Saudi population. We mined next generation sequencing (NGS) data from 11,889 unrelated Saudi nationals, to determine the presence and frequencies of known functional SNP variants in 8 clinically relevant pharmacogenes (CYP2C9, CYP2C19, CYP3A5, CYP4F2, VKORC1, DPYD, TPMT and NUDT15), recommended by the Clinical Pharmacogenetics Implementation Consortium (CPIC), and collectively identified 82 such star alleles. Functionally significant pharmacogenetic variants were prevalent especially in CYP genes (excluding CYP3A5), with 10–44.4% of variants predicted to be inactive or to have decreased activity. In CYP3A5, inactive alleles (87.5%) were the most common. Only 1.8%, 0.7% and 0.7% of NUDT15, TPMT and DPYD variants respectively, were predicted to affect gene activity. In contrast, VKORC1 was found functionally, to be highly polymorphic with 53.7% of Saudi individuals harboring variants predicted to result in decreased activity and 31.3% having variants leading to increased metabolic activity. Furthermore, among the 8 pharmacogenes studied, we detected six rare variants with an aggregated frequency of 1.1%, that among several other ethnicities, were uniquely found in Saudi population. Similarly, within our cohort, the 8 pharmacogenes yielded forty-six novel variants predicted to be deleterious. Based upon our findings, 99.2% of individuals from the Saudi population carry at least one actionable pharmacogenetic variant.

Stem Region of tRNA Genes Favors Transition Substitution Towards Keto Bases in Bacteria.

Transversion and transition mutations have variable effects on the stability of RNA secondary structure considering that the former destabilizes the double helix geometry to a greater extent by introducing purine:purine (R:R) or pyrimidine:pyrimidine (Y:Y) base pairs. Therefore, transversion frequency is likely to be lower than that of transition in the secondary structure regions of RNA genes. Here, we performed an analysis of transition and transversion frequencies in tRNA genes defined well with secondary structure and compared with the intergenic regions in five bacterial species namely Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Staphylococcus aureus and Streptococcus pneumoniae using a large genome sequence data set. In general, the transversion frequency was observed to be lower than that of transition in both tRNA genes and intergenic regions. The transition to transversion ratio was observed to be greater in tRNA genes than that in the intergenic regions in all the five bacteria that we studied. Interestingly, the intraspecies base substitution analysis in tRNA genes revealed that non-compensatory substitutions were more frequent than compensatory substitutions in the stem region. Further, transition to transversion ratio in the loop region was observed to be significantly lesser than that among the non-compensatory substitutions in the stem region. This indicated that the transversion is more deleterious than transition in the stem regions. In addition, substitutions from amino bases (A/C) to keto bases (G/T) were also observed to be more than the reverse substitutions in the stem region. Substitution from amino bases to keto bases are likely to facilitate the stable G:U pairing unlike the reverse substitution that facilitates the unstable A:C pairing in the stem region of tRNA. This work provides additional support that the secondary structure of tRNA molecule is what drives the different substitutions in its gene sequence.

Kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections.

When indexing large collections of short-read sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees and variants, BIGSI) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of k-mers which approximates the desired set of all the non-erroneous k-mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant k-mers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. We propose kmtricks, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are (i) an efficient method for jointly counting k-mers across multiple samples, including a streamlined Bloom filter construction by directly counting, partitioning and sorting hashes instead of k-mers, which is approximately four times faster than state-of-the-art tools; (ii) a novel technique that takes advantage of joint counting to preserve low-abundant k-mers present in several samples, improving the recovery of non-erroneous k-mers. Our experiments highlight that this technique preserves around 8× more k-mers than the usual yet crude filtering of low-abundance k-mers in a large metagenomics dataset. https://github.com/tlemane/kmtricks. Supplementary data are available at Bioinformatics Advances online.

Translation of DNA Sequence to Chemical Structure in DNA-Encoded Libraries.

Analysis of DEL selection results requires the generation of large DNA sequence data sets and the conversion of DNA sequence to chemical structure. SAM and BAM formats are the generic formats for storing sequence alignment results. However, such formats are not directly applicable for analysis of DEL selection results due to the lack of reference sequences. In this protocol, reference sequences are constructed from DEL structure files using the central dogma of molecular biology analogy. Enabling the use of SAM and BAM formats drastically facilitates data access while also reducing data storage requirements and permits the efficient translation of DNA sequence to chemical structure of DEL compounds.

The impact of maternal age on gene expression during the GV to MII transition in euploid human oocytes.

STUDY QUESTIONAre there age-related differences in gene expression during the germinal vesicle (GV) to metaphase II (MII) stage transition in euploid human oocytes?SUMMARY ANSWERA decrease in mitochondrial-related transcripts from GV to MII oocytes was observed, with a much greater reduction in MII oocytes with advanced age.WHAT IS KNOWN ALREADYEarly embryonic development is dependent on maternal transcripts accumulated and stored within the oocyte during oogenesis. Transcriptional activity of the oocyte, which dictates its ultimate developmental potential, may be influenced by age and explain the reduced competence of advanced maternal age (AMA) oocytes compared with the young maternal age (YMA). Gene expression has been studied in human and animal oocytes; however, RNA sequencing could provide further insights into the transcriptome profiling of GV and in vivo matured MII euploid oocytes of YMA and AMA patients.STUDY DESIGN, SIZE, DURATIONFifteen women treated for infertility in a single IVF unit agreed to participate in this study. Five GV and 5 MII oocytes from 6, 21–26 years old women (YMA cohort) and 5 GV and 6 MII oocytes from 6, 41–44 years old women (AMA cohort) undergoing IVF treatment were donated. The samples were collected within a time frame of 4 months. RNA was isolated and deep sequenced at the single-cell level. All donors provided either GV or MII oocytes.PARTICIPANTS/MATERIALS, SETTING, METHODSCumulus dissection from donated oocytes was performed 38 h after hCG injection, denuded oocytes were inserted into lysis buffer supplemented with RNase inhibitor. The samples were stored at −80°C until further use. Isolated RNA from GV and MII oocytes underwent library preparation using an oligo deoxy-thymidine (dT) priming approach (SMART-Seq v4 Ultra Low Input RNA assay; Takara Bio, Japan) and Nextera XT DNA library preparation assay (Illumina, USA) followed by deep sequencing. Data processing, quality assessment and bioinformatics analysis were performed using source-software, mainly including FastQC, HISAT2, StringTie and edgeR, along with functional annotation analysis, while scploid R package was employed to determine the ploidy status.MAIN RESULTS AND THE ROLE OF CHANCEFollowing deep sequencing of single GV and MII oocytes in both YMA and AMA cohorts, several hundred transcripts were found to be expressed at significantly different levels. When YMA and AMA MII oocyte transcriptomes were compared, the most significant of these were related to mitochondrial structure and function, including biological processes, mitochondrial respiratory chain complex I assembly and mitochondrial translational termination (false discovery rate (FDR) 6.0E−10 to 1.2E−7). These results indicate a higher energy potential of the YMA MII cohort that is reduced with ageing. Other biological processes that were significantly higher in the YMA MII cohort included transcripts involved in the translation process (FDR 1.9E−2). Lack of these transcripts could lead to inappropriate protein synthesis prior to or upon fertilisation of the AMA MII oocytes.LARGE SCALE DATAThe RNA sequencing data were deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo), under the accession number: GSE164371.LIMITATIONS, REASONS FOR CAUTIONThe relatively small sample size could be a reason for caution. However, the RNA sequencing results showed homogeneous clustering with low intra-group variation and five to six biological replicates derived from at least three different women per group minimised the potential impact of the sample size.WIDER IMPLICATIONS OF THE FINDINGSUnderstanding the effects of ageing on the oocyte transcriptome could highlight the mechanisms involved in GV to MII transition and identify biomarkers that characterise good MII oocyte quality. This knowledge has the potential to guide IVF regimes for AMA patients.STUDY FUNDING/COMPETING INTEREST(S)This work was supported by the Medical Research Council (MRC Grant number MR/K020501/1).

RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We formulate an objective function by adding l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop is able to annotate the unknown cells with high accuracy.

Temperature predicts the rate of molecular evolution in Australian Eugongylinae skinks.

Temperature differences over time and space have been hypothesized to cause variation in the rate of molecular evolution of species, but empirical evidence is mixed. To further test this hypothesis, we utilized a large exon-capture sequence data of Australian Eugongylinae skinks, exemplifying a radiation of temperature-sensitive ectotherms spanning a large latitudinal gradient. The association between temperature (and other species traits) and long-term substitution rate was assessed based on 1268 sequenced exons of 44 species pairs from the Eugongylinae subfamily using regression analyses. Temperature is the strongest, positively correlated predictor of variation in substitution rate across the Australian Eugongylinae. It explains 45% of variation in synonymous substitution rate, and 11% after controlling for all the other factors. Synonymous substitution rate is also negatively associated with body size, with a 6% variation explained by body size after controlling for the effects of temperature. Other factors are not associated with synonymous substitution rate after controlling for temperature. Overall, this study points to temperature as a strong predictor of the molecular evolution rate in the Eugongylinae subfamily, and demonstrates the power of large-scale exonic data to identify correlates of the rate of molecular evolution.

Duo: A Signature Based Method to Batch-Analyze Functional Similarities of Proteins.

With the rapid advancement of sequencing technology, handling of large sequencing data to analyze for protein coding capacity and functionality of predicted proteins has become an urgent demand. There is a lack of simple and effective tools to functionally annotate large number of unknown proteins in a personalized and customized workflow. To address this, we developed Duo, which batch-analyze functional similarities of predicted proteins. Duo can screen query proteins with specific characteristics based on highly flexible and customizable reference inputs from the user. In the current study, Duo was applied to screen for virulence associated proteins in the genome-sequence of Salmonella Typhimurium. Based on the analysis, recommendation for choice of Seed_database in order to get a reasonable number of predicted proteins for further analysis, and recommendation for preparing a Reference_proteins set for Duo was given. Delta-bitscore analysis was shown to be useful tool to focus the follow-up on predicted proteins. A successful screen for virulence proteins in the bacterial genome-sequence was further performed in a selection of 32 pathogenic bacteria, documenting the ability of Duo to work on a broad collection of bacteria. We anticipate that Duo will be a useful auxiliary tool for personalized and customized protein function research in the future.

Species status evaluation of Lirceus usdagalan, L. culveri, and L. hargeri populations (Isopoda; Asellidae) based on a large scale next-generation sequence data set

Species status evaluation of Lirceus usdagalan, L. culveri, and L. hargeri populations (Isopoda; Asellidae) based on a large scale next-generation sequence data set

Evolution of host use in fungivorous ciid beetles (Coleoptera: Ciidae): Molecular phylogeny focusing on Japanese taxa

Consumer–resource interactions between trophic levels are ubiquitous and important factors in shaping the diversity of insects. However, dietary patterns such as host specificity and conservatism have been insufficiently examined in fungivorous insects. Here we reconstructed the evolutionary history of host use in fungivorous ciid beetles (Coleoptera: Ciidae) and tested for host conservatism. Phylogenetic relationships among 49 species from Japan were inferred by using a large sequence data set from ultraconserved elements (UCEs). In addition, sequences of three genes (COI, 28S rRNA, 18S rRNA) were analyzed to reconstruct the phylogeny for 130 OTUs from a broader range of taxa and geographic regions using the UCE tree as a backbone topology. We found that Ciini and Orophiini are not recovered as reciprocally monophyletic groups. As previously suggested, the largest genus Cis Latreille was also not monophyletic. Ancestral-state reconstruction of host use in both datasets showed that Ciidae species were clustered by host-use group across the tree. This pattern was confirmed by the significantly lower transition rate compared with expectations under the random shift hypothesis. The observed conservatism in host use implied these beetles possess unique adaptations to specific fungal taxa, just as herbivorous insects are adapted to specific plant taxa.

Next-Generation Sequencing Technologies for Environmental DNA as an Efficient Bio Indicator for Bacterial Biodiversity in Tigris River, Iraq

Access to multiple, large sequence data volumes, as well as further improvements in read length and sequence platform efficiency, lead to better sample diversity. In order to assess the ability of Next Generation Sequence (NGS) approach to establish and evaluate the environmental molecular diagnostics, directly monitor bulk bacterial communities, the Illumina system of bacterial 16S rRNA gene amplicon was investigated in total bacterial communities in water samples. The dominant bacterial species can be identified through this method. NGS data will also allow the identification of drivers of the composition of bacterial communities during water treatment and distribution in combination with metadata, demonstrating the power of this approach to track bacterial regrowth and contamination in technical systems.

Profiles of alternative splicing landscape in breast cancer and their clinical significance: an integrative analysis based on large-sequencing data

BackgroundAlternative splicing (AS) is closely correlated with the initiation and progression of carcinoma. The systematic analysis of its biological and clinical significance in breast cancer (BRCA) is, however, lacking.MethodsClinical data and RNA-seq were obtained from the TCGA dataset and differentially expressed AS (DEAS) events between tumor and paired normal BRCA tissues were identified. Enrichment analysis was then used to reveal the potential biological functions of DEAS events. We performed protein-protein interaction (PPI) analysis of DEAS events by using STRING and the correlation network between splicing factors (SFs) and AS events was constructed. The LASSO Cox model, Kaplan-Meier and log-rank tests were used to construct and evaluate DEAS-related risk signature, and the association between DEAS events and clinicopathological features were then analyzed.ResultsAfter strict filtering, 35,367 AS events and 973 DEAS events were detected. DEAS corresponding genes were significantly enriched in pivotal pathways including cell adhesion, cytoskeleton organization, and extracellular matrix organization. A total of 103 DEAS events were correlated with disease free survival. The DEAS-related risk signature stratified BRCA patients into two groups and the area under curve (AUC) was 0.754. Moreover, patients in the high-risk group had enriched basel-like subtype, advanced clinical stages, proliferation, and metastasis potency.ConclusionsCollectively, the profile of DEAS landscape in BRCA revealed the potential biological function and prognostic value of DEAS events.

91 Electronic probes for assessment of changes in gastrointestinal microbiome in nursery pigs

Abstract Gut microbiota play an important role in extraction, synthesis and absorption of nutrients. Commensal bacteria prevent pathogenic bacteria colonization and maintain intestinal epithelium integrity. The most common families of commensal bacteria in nursery pigs are Prevotellaceae, Clostridiaceae, Erysipelotrichaceae, Lachnospitaceae, Lactobacillaceae, Ruminicoccaceae and Streptoccocaceae. Understanding the microbial abundance shifts that causes health disruption leading to diarrhea and stunted growth performance can be of great benefit for developing mitigation strategies. Next generation sequencing (NGS) technology facilitates metagenomic approaches, developing sequencing profile representing any and all organisms within a sample. Electronic-probe Diagnostic Nucleic acid Analysis (EDNA) is a bioinformatic tool originally developed to detect species-specific plant pathogen targets in metagenomic databases. EDNA has been shown to reduce time to detect microbial signatures in large metagenomic sequence data. However, it has not previously been used as a metagenomic tool for assessing microbiome composition at the family level. Therefore, a metagenomic sequencing based in silico detection of gut microbiota using E-probes of the seven most common commensal families was developed and further validated in vitro. E-probes were designed from the selected families as follows, Prevotellaceae (89,565), Clostridiaceae (58,554), Erysipelotrichaceae (195), Lachnospitaceae (87), Lactobacillaceae (211,507), Ruminicoccaceae (14,575) and Streptoccocaceae (54,632). Fecal metagenomes of nursery pigs from 0, 7, 14, and 21 d were used to validate the E-probes. The hits were able to detect the relative abundance variations of the 4-time periods. The results between hits and reads were as follows, Prevotellaceae (r2 = 0.98), Clostridiaceae (r2 = 0.99), Erysipelotrichaceae (r2 = 0.99), Lachnospitaceae (r2 = 0.99), Lactobacillaceae (r2 = 0.91), Ruminicoccaceae (r2 = 0.99) and Streptoccocaceae (r2 = 0.98). These results validate in silico usage of E-probes to detect the relative abundance variations in gut microbiota. Further in vitro validation will be performed to assess the microbial changes related to diet in nursery pigs.

A population genomics approach to uncover the CNVs, and their evolutionary significance, hidden in reduced-representation sequencing data sets.

The importance of structural variation in adaptation and speciation is becoming increasingly evident in the literature. Among SVs, copy number variants (CNVs) are known to affect phenotypes through changes in gene expression and can potentially reduce recombination between alleles with different copy numbers. However, little is known about their abundance, distribution and frequency in natural populations. In a "From the Cover" article in this issue of Molecular Ecology, Dorant et al. (2020) present a new cost-effective approach to genotype copy number variants (CNVs) from large reduced-representation sequencing (RRS) data sets in nonmodel organisms, and thus to analyse sequence and structural variation jointly. They show that in American lobsters (Homarus americanus), CNVs exhibit strong population structure and several significant associations with annual variance in sea surface temperature, while SNPs fail to uncover any population structure or genotype-environment associations. Their results clearly illustrate that structural variants like CNVs can potentially store important information on differentiation and adaptive differences that cannot be retrieved from the analysis of sequence variation alone. To better understand the factors affecting the evolution of CNVs and their role in adaptation and speciation, we need to compare and synthesize data from a wide variety of species with different demographic histories and genome structure. The approach developed by Dorant et al. (2020) now allows to gain crucial knowledge on CNVs in a cost-effective way, even in species with limited genomic resources.

Essentials in Metagenomics (Part II)

With the development of high-throughput next generation sequencing technologies and the technical advances to generate high-quality sequencing data, the bottleneck in implementing metagenomics for economic and social purposes in Latin America has shifted from obtaining DNA sequences to post-sequencing data analysis. Latin American countries still face challenges to develop and implement strategies for local data processing and data mining due to the limited bioinformatics capacity. Although many large computing grids already exist, outsourcing analyses and computing is not always the best solution since data transfer requires significant bandwidth due to the file sizes, and more importantly, it limits the training of local researchers to perform cutting-edge bioinformatics. Therefore, developing learning material in data processing and analysis in metagenomics is critical to the formation of new highly-skilled bioinformaticians. The present document is a tutorial in essential aspects of metagenomics data processing (post- sequencing) which was structured based on an extensive literature review, with the goal of presenting the current bioinformatics tools and workflows for extracting relevant, biological information out of a large sequencing data set. This is the second of a two-part series of documents designed to provide the essential background (experimental and computational) for users to approach the field of metagenomics. Users will find an overview of the different bioinformatic analyses that are commonly performed in metagenomic studies, including quality control, decontamination, coverage estimation, assembly, recovery of MAGs (metagenome- assembled genomes), taxonomic and functional classification of reads and contigs, and strain- level comparative analysis, among many other relevant topics. Users will also find rich discussions on the advantages and limitations that the different tools and methods offer and will be provided with external links and websites where additional information can be found.

NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones

The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to address the system resources to the correct application, making the system more intelligent and efficient. Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequences of data, outperforming all previous classification and prediction models. A recurrent neural network (RNN) is an artificial neural network associated with sequence models, and it can recognize patterns in sequences. One of the areas that use RNN is language modeling (LM). Given an arrangement of words, LM can learn how the words are organized in sentences, making it possible to predict the next word given a group of previous words. We propose building a predictive model inspired by LM. However, instead of using words, we will use previous applications to predict the next application. Moreover, some context features, such as timestamp and energy record, will be included in the prediction model to evaluate the impact of the features on the performance. We will provide the following application prediction result and extend it to the top-k possible candidates for the next application.