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Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.

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The nonsense-mediated RNA decay (NMD) pathway is a crucial mechanism of mRNA quality control. Current annotations of NMD substrate RNAs are rarely data-driven, but use generally established rules. We present a data set with four cell lines and combinations for SMG5, SMG6, and SMG7 knockdowns or SMG7 knockout. Based on this data set, we implemented a workflow that combines Nanopore and Illumina sequencing to assemble a transcriptome, which is enriched for NMD target transcripts. Moreover, we use coding sequence information (CDS) from Ensembl, Gencode consensus Ribo-seq ORFs, and OpenProt to enhance the CDS annotation of novel transcript isoforms. In summary, 302,889 transcripts were obtained from the transcriptome assembly process, out of which 24% are absent from Ensembl database annotations, 48,213 contain a premature stop codon, and 6433 are significantly upregulated in three or more comparisons of NMD active versus deficient cell lines. We present an in-depth view of these results through the NMDtxDB database, which is available at https://shiny.dieterichlab.org/app/NMDtxDB, and supports the study of NMD-sensitive transcripts. We open sourced our implementation of the respective web-application and analysis workflow at https://github.com/dieterich-lab/NMDtxDB and https://github.com/dieterich-lab/nmd-wf.

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Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

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We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

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Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAG reconstruction but also emphasized several limitations, such as the nonbinning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used; however, it still remains unclear which assembly strategy, in combination with which binning approach, offers the best performance for MAG recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to coassemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets. We propose a novel approach to automate coassembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous coassembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing coassembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.

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The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

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Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols: 4sU-tagging biochemical enrichment, including spike-in RNA controls, SLAM-seq, TimeLapse-seq and TUC-seq. All protocols are evaluated based on practical considerations, conversion efficiency and wet lab requirements to handle hazardous substances. We also compute decay rate estimates and confidence intervals for each protocol using two alternative statistical frameworks, pulseR and GRAND-SLAM, for over 11 600 human genes and evaluate the underlying computational workflows for their robustness and ease of use. Overall, we demonstrate a high inter-method reliability across eight use case scenarios. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.

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The study of sex determination and sex chromosome organization in nonmodel species has long been technically challenging, but new sequencing methodologies now enable precise and high-throughput identification of sex-specific genomic sequences. In particular, restriction site-associated DNA sequencing (RAD-Seq) is being extensively applied to explore sex determination systems in many plant and animal species. However, software specifically designed to search for and visualize sex-biased markers using RAD-Seq data is lacking. Here, we present RADSex, a computational analysis workflow designed to study the genetic basis of sex determination using RAD-Seq data. RADSex is simple to use, requires few computational resources, makes no prior assumptions about the type of sex-determination system or structure of the sex locus, and offers convenient visualization through a dedicated R package. To demonstrate the functionality of RADSex, we re-analysed a published data set of Japanese medaka, Oryzias latipes, where we uncovered a previously unknown Y chromosome polymorphism. We then used RADSex to analyse new RAD-Seq data sets from 15 fish species spanning multiple taxonomic orders. We identified the sex determination system and sex-specific markers in six of these species, five of which had no known sex-markers prior to this study. We show that RADSex greatly facilitates the study of sex determination systems in nonmodel species thanks to its speed of analyses, low resource usage, ease of application and visualization options. Furthermore, our analysis of new data sets from 15 species provides new insights on sex determination in fish.

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Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.

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The identification of strategies by which to increase the representation of women and increase diversity in STEM fields (science, technology, engineering and mathematics), including medicine, has been a pressing matter for global agencies including the European Commission, UNESCO and numerous international scientific societies. In my role as UCL training lead for CompBioMed, a European Commission Horizon 2020-funded Centre of Excellence in Computational Biomedicine (compbiomed.eu), and as Head of Teaching for Molecular Biosciences at UCL from 2010 to 2019, I have integrated research and teaching to lead the development of high-performance computing (HPC)-based education targeting medical students and undergraduate students studying biosciences in a way that is explicitly integrated into the existing university curriculum as a credit-bearing module. One version of the credit-bearing module has been specifically designed for medical students in their pre-clinical years of study and one of the unique features of the course is the integration of clinical and computational aspects, with students obtaining and processing clinical samples and then interrogating the results computationally using code that was ported to HPC at CompBioMed's HPC Facility core partners (EPCC (UK), SURFsara (The Netherlands) and the Barcelona Supercomputing Centre (Spain)). Another version of the credit-bearing module has, over the course of this project, evolved into a replacement for the third year research project course for undergraduate biochemistry, biotechnology and molecular biology students, providing students with the opportunity to design and complete an entire specialist research project from the formulation of experimental hypotheses to the investigation of these hypotheses in a way that involves the integration of experimental and HPC-based computational methodologies. Since 2017-2018, these UCL modules have been successfully delivered to over 350 students-a cohort with a demographic of greater than 50% female. CompBioMed's experience with these two university modules has enabled us to distil our methodology into an educational template that can be delivered at other universities in Europe and worldwide. This educational approach to training enables new communities of practice to effectively engage with HPC and reveals a means by which to improve the underrepresentation of women in supercomputing.

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Failures to reproduce research findings across scientific disciplines from psychology to physics have garnered increasing attention in recent years. External replication of published findings by outside investigators has emerged as a method to detect errors and bias in the published literature. However, some studies influence policy and practice before external replication efforts can confirm or challenge the original contributions. Uncovering and resolving errors before publication would increase the efficiency of the scientific process by increasing the accuracy of published evidence. Here we summarize the rationale and best practices for internal replication, a process in which multiple independent data analysts replicate an analysis and correct errors prior to publication. We explain how internal replication should reduce errors and bias that arise during data analyses and argue that it will be most effective when coupled with pre-specified hypotheses and analysis plans and performed with data analysts masked to experimental group assignments. By improving the reproducibility of published evidence, internal replication should contribute to more rapid scientific advances.

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We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

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Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R 2) between the predicted and measured DMY across three seasons with R 2 between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.

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Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.

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Cysteine oxidative modification of cellular proteins is crucial for many aspects of cardiac hypertrophy development. However, integrated dissection of multiple types of cysteine oxidative post-translational modifications (O-PTM) of proteomes in cardiac hypertrophy is currently missing. Here we developed a novel discovery platform that encompasses a customized biotin switch-based quantitative proteomics pipeline and an advanced analytic workflow to comprehensively profile the landscape of cysteine O-PTM in an ISO-induced cardiac hypertrophy mouse model. Specifically, we identified a total of 1655 proteins containing 3324 oxidized cysteine sites by at least one of the following three modifications: reversible cysteine O-PTM, cysteine sulfinylation (CysSO2H), and cysteine sulfonylation (CysSO3H). Analyzing the hypertrophy signatures that are reproducibly discovered from this computational workflow unveiled four biological processes with increased cysteine O-PTM. Among them, protein phosphorylation, creatine metabolism, and response to elevated Ca2+ pathways exhibited an elevation of cysteine O-PTM in early stages, whereas glucose metabolism enzymes were increasingly modified in later stages, illustrating a temporal regulatory map in cardiac hypertrophy. Our cysteine O-PTM platform depicts a dynamic and integrated landscape of the cysteine oxidative proteome, through the extracted molecular signatures, and provides critical mechanistic insights in cardiac hypertrophy. Data are available via ProteomeXchange with identifier PXD010336.

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Multi-scale modeling of the musculoskeletal system plays an essential role in the deep understanding of complex mechanisms underlying the biological phenomena and processes such as bone metabolic processes. Current multi-scale models suffer from the isolation of sub-models at each anatomical scale. The objective of this present work was to develop a new fully integrated computational workflow for simulating bone metabolic processes at multi-scale levels. Organ-level model employs multi-body dynamics to estimate body boundary and loading conditions from body kinematics. Tissue-level model uses finite element method to estimate the tissue deformation and mechanical loading under body loading conditions. Finally, cell-level model includes bone remodeling mechanism through an agent-based simulation under tissue loading. A case study on the bone remodeling process located on the human jaw was performed and presented. The developed multi-scale model of the human jaw was validated using the literature-based data at each anatomical level. Simulation outcomes fall within the literature-based ranges of values for estimated muscle force, tissue loading and cell dynamics during bone remodeling process. This study opens perspectives for accurately simulating bone metabolic processes using a fully integrated computational workflow leading to a better understanding of the musculoskeletal system function from multiple length scales as well as to provide new informative data for clinical decision support and industrial applications.

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