Motivation: Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another, and vice versa. Results: To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with an arbitrary distribution of reading frames. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN/dS) in the respective reading frames. Availability and implementation: Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License (GPL) version 3, and is available at https://github.com/PoonLab/HexSE. <<<

In the evolutionary model of dosage compensation, per-allele expression level of the X chromosome has been proposed to have twofold up-regulation to compensate its dose reduction in males (XY) compared to females (XX). However, the expression regulation of X-linked genes is still controversial, and comprehensive evaluations are still lacking. By integrating multi-omics datasets in mammals, we investigated the expression ratios including X to autosomes (X:AA ratio) and X to orthologs (X:XX ratio) at the transcriptome, translatome, and proteome levels. We revealed a dynamic spatial-temporal X:AA ratio during development in humans and mice. Meanwhile, by tracing the evolution of orthologous gene expression in chickens, platypuses, and opossums, we found a stable expression ratio of X-linked genes in humans to their autosomal orthologs in other species (X:XX ≈ 1) across tissues and developmental stages, demonstrating stable dosage compensation in mammals. We also found that different epigenetic regulations contributed to the high tissue specificity and stage specificity of X-linked gene expression, thus affecting X:AA ratios. It could be concluded that the dynamics of X:AA ratios were attributed to the different gene contents and expression preferences of the X chromosome, rather than the stable dosage compensation. <<<

Graham Scott Erwin , Gamze Gursoy , Rashid Al-Abri , Ashwini Suriyaprakash , Egor Dolzhenko , Kevin Zhu , Christian R Hoerner , Shannon M White , Lucia Ramirez , Ananya Vadlakonda , Alekhya Vadlakonda , Konor von Kraut , Julia Park , Charlotte M Brannon , Daniel A Sumano , Raushun A Kirtikar , Alicia A Erwin , Thomas J Metzner , Ryan K. C. Yuen , Alice C Fan , John T Leppert , Michael A Eberle , Mark Gerstein , Michael P Snyder <<<

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs (STRs), a phenomenon termed microsatellite instability (MSI); however larger repeat expansions have not been systematically analyzed in cancer. Here, we identified TR expansions in 2,622 cancer genomes, spanning 29 cancer types. In 7 cancer types, we found 160 recurrent repeat expansions (rREs); most of these (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with an enrichment near candidate cis-regulatory elements, suggesting a role in gene regulation. One rRE located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, targeting cells harboring this rRE with a rationally designed, sequence-specific DNA binder led to a dose-dependent decrease in cell proliferation. Overall, our results demonstrate that rREs are an important but unexplored source of genetic variation in human cancers, and we provide a comprehensive catalog for further study. <<<

BACKGROUND: Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations.RESULTS: Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets.CONCLUSIONS: Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions. <<<

Konrad J Karczewski, Matthew Solomonson, Katherine R Chao, Julia K Goodrich, Grace Tiao, Wenhan Lu, Bridget M Riley-Gillis, Ellen A Tsai, Hye In Kim, Xiuwen Zheng, Fedik Rahimov, Sahar Esmaeeli, A Jason Grundstad, Mark Reppell, Jeff Waring, Howard Jacob, David Sexton, Paola G Bronson, Xing Chen, Xinli Hu, Jacqueline I Goldstein, Daniel King, Christopher Vittal, Timothy Poterba, Duncan S Palmer, Claire Churchhouse, Daniel P Howrigan, Wei Zhou, Nicholas A Watts, Kevin Nguyen, Huy Nguyen, Cara Mason, Christopher Farnham, Charlotte Tolonen, Laura D Gauthier, Namrata Gupta, Daniel G MacArthur, Heidi L Rehm, Cotton Seed, Anthony A Philippakis, Mark J Daly, J Wade Davis, Heiko Runz, Melissa R Miller, Benjamin M Neale <<<

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results. <<<

Mitchell E Fane, Yash Chhabra, Gretchen M Alicea, Devon A Maranto, Stephen M Douglass, Marie R Webster, Vito W Rebecca, Gloria E Marino, Filipe Almeida, Brett L Ecker, Daniel J Zabransky, Laura Hüser, Thomas Beer, Hsin-Yao Tang, Andrew Kossenkov, Meenhard Herlyn, David W Speicher, Wei Xu, Xiaowei Xu, Elizabeth M Jaffee, Julio A Aguirre-Ghiso, Ashani T Weeraratna <<<

Disseminated cancer cells from primary tumours can seed in distal tissues, but may take several years to form overt metastases, a phenomenon that is termed tumour dormancy. Despite its importance in metastasis and residual disease, few studies have been able to successfully characterize dormancy within melanoma. Here we show that the aged lung microenvironment facilitates a permissive niche for efficient outgrowth of dormant disseminated cancer cells-in contrast to the aged skin, in which age-related changes suppress melanoma growth but drive dissemination. These microenvironmental complexities can be explained by the phenotype switching model, which argues that melanoma cells switch between a proliferative cell state and a slower-cycling, invasive state. It was previously shown that dermal fibroblasts promote phenotype switching in melanoma during ageing. We now identify WNT5A as an activator of dormancy in melanoma disseminated cancer cells within the lung, which initially enables the efficient dissemination and seeding of melanoma cells in metastatic niches. Age-induced reprogramming of lung fibroblasts increases their secretion of the soluble WNT antagonist sFRP1, which inhibits WNT5A in melanoma cells and thereby enables efficient metastatic outgrowth. We also identify the tyrosine kinase receptors AXL and MER as promoting a dormancy-to-reactivation axis within melanoma cells. Overall, we find that age-induced changes in distal metastatic microenvironments promote the efficient reactivation of dormant melanoma cells in the lung. <<<

Daniel Bottomly, Nicola Long, Anna Reister Schultz, Stephen E Kurtz, Cristina E Tognon, Kara Johnson, Melissa Abel, Anupriya Agarwal, Sammantha Avaylon, Erik Benton, Aurora Blucher, Uma Borate, Theodore P Braun, Jordana Brown, Jade Bryant, Russell Burke, Amy Carlos, Bill H Chang, Hyun Jun Cho, Stephen Christy, Cody Coblentz, Aaron M Cohen, Amanda d'Almeida, Rachel Cook, Alexey Danilov, Kim-Hien T Dao, Michie Degnin, James Dibb, Christopher A Eide, Isabel English, Stuart Hagler, Heath Harrelson, Rachel Henson, Hibery Ho, Sunil K Joshi, Brian Junio, Andy Kaempf, Yoko Kosaka, Ted Laderas, Matt Lawhead, Hyunjung Lee, Jessica T Leonard, Chenwei Lin, Evan F Lind, Selina Qiuying Liu, Pierrette Lo, Marc M Loriaux, Samuel Luty, Julia E Maxson, Tara Macey, Jacqueline Martinez, Jessica Minnier, Andrea Monteblanco, Motomi Mori, Quinlan Morrow, Dylan Nelson, Justin Ramsdill, Angela Rofelty, Alexandra Rogers, Kyle A Romine, Peter Ryabinin, Jennifer N Saultz, David A Sampson, Samantha L Savage, Robert Schuff, Robert Searles, Rebecca L Smith, Stephen E Spurgeon, Tyler Sweeney, Ronan T Swords, Aashis Thapa, Karina Thiel-Klare, Elie Traer, Jake Wagner, Beth Wilmot, Joelle Wolf, Guanming Wu, Amy Yates, Haijiao Zhang, Christopher R Cogle, Robert H Collins, Michael W Deininger, Christopher S Hourigan, Craig T Jordan, Tara L Lin, Micaela E Martinez, Rachel R Pallapati, Daniel A Pollyea, Anthony D Pomicter, Justin M Watts, Scott J Weir, Brian J Druker, Shannon K McWeeney, Jeffrey W Tyner <<<

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. <<<

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone. <<<

The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes. Despite their therapeutic potential, our map of these surface interactions remains incomplete. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention. <<<

Ryan L Collins, Joseph T Glessner, Eleonora Porcu, Maarja Lepamets, Rhonda Brandon, Christopher Lauricella, Lide Han, Theodore Morley, Lisa-Marie Niestroj, Jacob Ulirsch, Selin Everett, Daniel P Howrigan, Philip M Boone, Jack Fu, Konrad J Karczewski, Georgios Kellaris, Chelsea Lowther, Diane Lucente, Kiana Mohajeri, Margit Nõukas, Xander Nuttle, Kaitlin E Samocha, Mi Trinh, Farid Ullah, Urmo Võsa, Epi25 Consortium, Estonian Biobank Research Team, Matthew E Hurles, Swaroop Aradhya, Erica E Davis, Hilary Finucane, James F Gusella, Aura Janze, Nicholas Katsanis, Ludmila Matyakhina, Benjamin M Neale, David Sanders, Stephanie Warren, Jennelle C Hodge, Dennis Lal, Douglas M Ruderfer, Jeanne Meck, Reedik Mägi, Tõnu Esko, Alexandre Reymond, Zoltán Kutalik, Hakon Hakonarson, Shamil Sunyaev, Harrison Brand, Michael E Talkowski <<<

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics. <<<

Paul Petrus, Marlene Cervantes, Muntaha Samad, Tomoki Sato, Alina Chao, Shogo Sato, Kevin B Koronowski, Grace Park, Yasmine Alam, Niklas Mejhert, Marcus M Seldin, José Manuel Monroy Kuhn, Kenneth A Dyar, Dominik Lutter, Pierre Baldi, Peter Kaiser, Cholsoon Jang, Paolo Sassone-Corsi <<<

OBJECTIVE: The circadian clock aligns physiology with the 24-hour rotation of Earth. Light and food are the main environmental cues (zeitgebers) regulating circadian rhythms in mammals. Yet, little is known about the interaction between specific dietary components and light in coordinating circadian homeostasis. Herein, we focused on the role of essential amino acids.METHODS: Mice were fed diets depleted of specific essential amino acids and their behavioral rhythms were monitored and tryptophan was selected for downstream analyses. The role of tryptophan metabolism in modulating circadian homeostasis was studied using isotope tracing as well as transcriptomic- and metabolomic- analyses.RESULTS: Dietary tryptophan depletion alters behavioral rhythms in mice. Furthermore, tryptophan metabolism was shown to be regulated in a time- and light- dependent manner. A multi-omics approach and combinatory diet/light interventions demonstrated that tryptophan metabolism modulates temporal regulation of metabolism and transcription programs by buffering photic cues. Specifically, tryptophan metabolites regulate central circadian functions of the suprachiasmatic nucleus and the core clock machinery in the liver.CONCLUSIONS: Tryptophan metabolism is a modulator of circadian homeostasis by integrating environmental cues. Our findings propose tryptophan metabolism as a potential point for pharmacologic intervention to modulate phenotypes associated with disrupted circadian rhythms. <<<

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3’-untranslated region (3’-UTR) of mRNA produces transcripts with shorter or longer 3’-UTR. Often, 3’-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3’-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3’-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3’-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3’-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations.MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3’-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3’-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3’-UTR annotation and read coverage on the 3’-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user’s manual are freely available at https://github.com/compbiolabucf/APA-Scan.ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3’-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3’-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3’ -UTR APA events and improve genome annotation.ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3’-UTR APA events. The pipeline integrates both RNA-seq and 3’-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots. <<<

SUMMARY: Bioinformatics applications increasingly rely on ad hoc disk storage of k-mer sets, e.g. for de Bruijn graphs or alignment indexes. Here, we introduce the K-mer File Format as a general lossless framework for storing and manipulating k-mer sets, realizing space savings of 3-5× compared to other formats, and bringing interoperability across tools.AVAILABILITY AND IMPLEMENTATION: Format specification, C++/Rust API, tools: https://github.com/Kmer-File-Format/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. <<<

Transposable elements (TEs), which make up almost half of the human genome, often display altered expression in cancers. Here, we review recent progress in elucidating the role of TEs as mediators of immune responses in cancer and discuss how novel therapeutic strategies can harness TE immunogenicity for cancer immunotherapy. <<<

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies. <<<

Genome assembly has been benefited from long-read sequencing technologies with higher accuracy and higher continuity. However, most human genome assembly require large amount of DNAs from homogeneous cell lines without keeping cell heterogeneities, since cell heterogeneity could profoundly affect haplotype assembly results. Herein, using single-cell genome long-read sequencing technology (SMOOTH-seq), we have sequenced K562 and HG002 cells on PacBio HiFi and Oxford Nanopore Technologies (ONT) platforms and conducted de novo genome assembly. For the first time, we have completed the human genome assembly with high continuity (with NG50 of ∼2 Mb using 95 individual K562 cells) at single-cell levels, and explored the impact of different assemblers and sequencing strategies on genome assembly. With sequencing data from 30 diploid individual HG002 cells of relatively high genome coverage (average coverage ∼41.7%) on ONT platform, the NG50 can reach over 1.3 Mb. Furthermore, with the assembled genome from K562 single-cell dataset, more complete and accurate set of insertion events and complex structural variations could be identified. This study opened a new chapter on the practice of single-cell genome de novo assembly. <<<

MOTIVATION: The growing use of next-generation sequencing and enlarged sequencing throughput require efficient short-read alignment, where seeding is one of the major performance bottlenecks. The key challenge in the seeding phase is searching for exact matches of substrings of short reads in the reference DNA sequence. Existing algorithms, however, present limitations in performance due to their frequent memory accesses.RESULTS: This article presents BWA-MEME, the first full-fledged short read alignment software that leverages learned indices for solving the exact match search problem for efficient seeding. BWA-MEME is a practical and efficient seeding algorithm based on a suffix array search algorithm that solves the challenges in utilizing learned indices for SMEM search which is extensively used in the seeding phase. Our evaluation shows that BWA-MEME achieves up to 3.45× speedup in seeding throughput over BWA-MEM2 by reducing the number of instructions by 4.60×, memory accesses by 8.77× and LLC misses by 2.21×, while ensuring the identical SAM output to BWA-MEM2.AVAILABILITY AND IMPLEMENTATION: The source code and test scripts are available for academic use at https://github.com/kaist-ina/BWA-MEME/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. <<<

It is critical to discover novel biomarkers of lung cancer for establishing economical technology for diagnosis of lung cancer. Our study has suggested that the autofluorescence (AF) of the skin may become a novel biomarker of this type: First, development of lung cancer led to a significant increase in the skin's green AF in a mouse model of lung cancer; second, lung cancer patients had significantly higher skin's green AF at certain positions compared with healthy volunteers and pulmonary infection patients; and third, using the skin's green AF intensity at dorsal centremetacarpus as the variable, the areas under curve (AUC) for differentiating lung cancer patients and pulmonary infection patients and for differentiating lung cancer patients and healthy volunteers was 0.871 and 0.813, respectively. Collectively, our study has indicated that the skin's green AF at dorsal centremetacarpus may become a novel biomarker for establishing a ground-breaking diagnostic strategy for lung cancer. <<<

The serotonin hypothesis of depression is still influential. We aimed to synthesise and evaluate evidence on whether depression is associated with lowered serotonin concentration or activity in a systematic umbrella review of the principal relevant areas of research. PubMed, EMBASE and PsycINFO were searched using terms appropriate to each area of research, from their inception until December 2020. Systematic reviews, meta-analyses and large data-set analyses in the following areas were identified: serotonin and serotonin metabolite, 5-HIAA, concentrations in body fluids; serotonin 5-HT receptor binding; serotonin transporter (SERT) levels measured by imaging or at post-mortem; tryptophan depletion studies; SERT gene associations and SERT gene-environment interactions. Studies of depression associated with physical conditions and specific subtypes of depression (e.g. bipolar depression) were excluded. Two independent reviewers extracted the data and assessed the quality of included studies using the AMSTAR-2, an adapted AMSTAR-2, or the STREGA for a large genetic study. The certainty of study results was assessed using a modified version of the GRADE. We did not synthesise results of individual meta-analyses because they included overlapping studies. The review was registered with PROSPERO (CRD42020207203). 17 studies were included: 12 systematic reviews and meta-analyses, 1 collaborative meta-analysis, 1 meta-analysis of large cohort studies, 1 systematic review and narrative synthesis, 1 genetic association study and 1 umbrella review. Quality of reviews was variable with some genetic studies of high quality. Two meta-analyses of overlapping studies examining the serotonin metabolite, 5-HIAA, showed no association with depression (largest n = 1002). One meta-analysis of cohort studies of plasma serotonin showed no relationship with depression, and evidence that lowered serotonin concentration was associated with antidepressant use (n = 1869). Two meta-analyses of overlapping studies examining the 5-HT receptor (largest n = 561), and three meta-analyses of overlapping studies examining SERT binding (largest n = 1845) showed weak and inconsistent evidence of reduced binding in some areas, which would be consistent with increased synaptic availability of serotonin in people with depression, if this was the original, causal abnormaly. However, effects of prior antidepressant use were not reliably excluded. One meta-analysis of tryptophan depletion studies found no effect in most healthy volunteers (n = 566), but weak evidence of an effect in those with a family history of depression (n = 75). Another systematic review (n = 342) and a sample of ten subsequent studies (n = 407) found no effect in volunteers. No systematic review of tryptophan depletion studies has been performed since 2007. The two largest and highest quality studies of the SERT gene, one genetic association study (n = 115,257) and one collaborative meta-analysis (n = 43,165), revealed no evidence of an association with depression, or of an interaction between genotype, stress and depression. The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations. Some evidence was consistent with the possibility that long-term antidepressant use reduces serotonin concentration. <<<

BACKGROUND: Genome-wide RNA-sequencing technologies are increasingly critical to a wide variety of diagnostic and research applications. RNA-seq users often first enrich for mRNA, with the most popular enrichment method being poly(A) selection. In many applications it is well-known that poly(A) selection biases the view of the transcriptome by selecting for longer tailed mRNA species.RESULTS: Here, we show that poly(A) selection biases Oxford Nanopore direct RNA sequencing. As expected, poly(A) selection skews sequenced mRNAs toward longer poly(A) tail lengths. Interestingly, we identify a population of mRNAs (> 10% of genes' mRNAs) that are inconsistently captured by poly(A) selection due to highly variable poly(A) tails, and demonstrate this phenomenon in our hands and in published data. Importantly, we show poly(A) selection is dispensable for Oxford Nanopore's direct RNA-seq technique, and demonstrate successful library construction without poly(A) selection, with decreased input, and without loss of quality.CONCLUSIONS: Our work expands the utility of direct RNA-seq by validating the use of total RNA as input, and demonstrates important technical artifacts from poly(A) selection that inconsistently skew mRNA expression and poly(A) tail length measurements. <<<