Linking genotypic and phenotypic changes in the E. coli long-term evolution experiment using metabolomics.

Changes in an organism's environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism's metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the long-term evolution experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.

Intestinal transit-amplifying cells require METTL3 for growth factor signaling and cell survival.

Intestinal epithelial transit-amplifying cells are essential stem progenitors required for intestinal homeostasis, but their rapid proliferation renders them vulnerable to DNA damage from radiation and chemotherapy. Despite these cells' critical roles in intestinal homeostasis and disease, few studies have described genes that are essential to transit-amplifying cell function. We report that RNA methyltransferase-like 3 (METTL3) is required for survival of transit-amplifying cells in the murine small intestine. Transit-amplifying cell death after METTL3 deletion was associated with crypt and villus atrophy, loss of absorptive enterocytes, and uniform wasting and death in METTL3-depleted mice. Sequencing of polysome-bound and methylated RNAs in enteroids and in vivo demonstrated decreased translation of hundreds of methylated transcripts after METTL3 deletion, particularly transcripts involved in growth factor signal transduction such as Kras. Further investigation verified a relationship between METTL3 and Kras methylation and protein levels in vivo. Our study identifies METTL3 as an essential factor supporting the homeostasis of small intestinal tissue via direct maintenance of transit-amplifying cell survival. We highlight the crucial role of RNA modifications in regulating growth factor signaling in the intestine with important implications for both homeostatic tissue renewal and epithelial regeneration.

Celf4 controls mRNA translation underlying synaptic development in the prenatal mammalian neocortex.

Abnormalities in neocortical and synaptic development are linked to neurodevelopmental disorders. However, the molecular and cellular mechanisms governing initial synapse formation in the prenatal neocortex remain poorly understood. Using polysome profiling coupled with snRNAseq on human cortical samples at various fetal phases, we identify human mRNAs, including those encoding synaptic proteins, with finely controlled translation in distinct cell populations of developing frontal neocortices. Examination of murine and human neocortex reveals that the RNA binding protein and translational regulator, CELF4, is expressed in compartments enriched in initial synaptogenesis: the marginal zone and the subplate. We also find that Celf4/CELF4-target mRNAs are encoded by risk genes for adverse neurodevelopmental outcomes translating into synaptic proteins. Surprisingly, deleting Celf4 in the forebrain disrupts the balance of subplate synapses in a sex-specific fashion. This highlights the significance of RNA binding proteins and mRNA translation in evolutionarily advanced synaptic development, potentially contributing to sex differences.

Evaluating single-cell variability in proteasomal decay.

Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.

A Cohort Study of the Diversity in Animated Films From 1937 to 2021: In a World Less Enchanted Can We Be More Encanto?

Background Exposure to gender stereotypes in the media can develop and reinforce these attitudes in children. Individuals who are overweight, have health conditions, or are from a minority ethnic group (IMEG) are both underrepresented and poorly portrayed in the media. Role models can raise the aspirations of young children both professionally and in taking ownership of their health. We aimed to assess how the portrayal and diversity of characters in Disney, Pixar, and Dreamworks animated films have changed over time. Method A cohort study of all main characters in Disney, Pixar, and Dreamworks feature-length, theatrical, animated films from 1937 to 2021 was conducted. The portrayal of characters (R-score divided into negative, neutral, and positive -1, 0, and 1, respectively) was scored. The proportion of individuals with certain protected characteristics (sex, increased body mass index, physical or mental health conditions, being from an IMEG or part of the lesbian, gay, bisexual, transexual, and queer community) was also recorded. Results In total, 116 films and 1,275 characters were included. From the 1930s to 2020s, the proportion of women in films increased (16.7% to 47.3%, p=0.008) and their representation was more positive (mean R-score = -0.10 (SD:0.692) versus 0.49 (SD:0.837), p<0.001, respectively). The portrayal of overweight individuals has improved to a neutral position (mean R-score: -0.67 to 0.0). Both physical and mental illnesses are better portrayed (mean R-score: -0.18 to 0.34, p=0.004 and 0.5 to 1.0, p= 0.019, respectively). IMEGs introduced in 1953 now play more than just negative roles (mean R-score = -1 to 0.76, p=0.008). There is only one explicitly stated homosexual character. The most diverse film is Encanto. Conclusion This is the first study to comprehensively assess the diversity of animated film characters. We have identified an improvement in diversity and the way diverse individuals are portrayed which we hope continues.

Mental health in autistic adults: A rapid review of prevalence of psychiatric disorders and umbrella review of the effectiveness of interventions within a neurodiversity informed perspective.

BACKGROUND: Autistic adults have high risk of mental ill-health and some available interventions have been associated with increased psychiatric diagnoses. Understanding prevalence of psychiatric diagnoses is important to inform the development of individualised treatment and support for autistic adults which have been identified as a research priority by the autistic community. Interventions require to be evaluated both in terms of effectiveness and regarding their acceptability to the autistic community. OBJECTIVE: This rapid review identified the prevalence of psychiatric disorders in autistic adults, then systematic reviews of interventions aimed at supporting autistic adults were examined. A rapid review of prevalence studies was completed concurrently with an umbrella review of interventions. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were followed, including protocol registration (PROSPERO#CRD42021283570). DATA SOURCES: MEDLINE, CINAHL, PsycINFO, and Cochrane Database of Systematic Reviews. STUDY ELIGIBILITY CRITERIA: English language; published 2011-2022; primary studies describing prevalence of psychiatric conditions in autistic adults; or systematic reviews evaluating interventions for autistic adults. APPRAISAL AND SYNTHESIS: Bias was assessed using the Prevalence Critical Appraisal Instrument and AMSTAR2. Prevalence was grouped according to psychiatric diagnosis. Interventions were grouped into pharmacological, employment, psychological or mixed therapies. Strength of evidence for interventions was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Autistic researchers within the team supported interpretation. RESULTS: Twenty prevalence studies were identified. Many included small sample sizes or failed to compare their sample group with the general population reducing validity. Prevalence of psychiatric diagnoses was variable with prevalence of any psychiatric diagnosis ranging from 15.4% to 79%. Heterogeneity was associated with age, diagnosis method, sampling methods, and country. Thirty-two systematic reviews of interventions were identified. Four reviews were high quality, four were moderate, five were low and nineteen critically low, indicating bias. Following synthesis, no intervention was rated as 'evidence based.' Acceptability of interventions to autistic adults and priorities of autistic adults were often not considered. CONCLUSIONS: There is some understanding of the scope of mental ill-health in autism, but interventions are not tailored to the needs of autistic adults, not evidence based, and may focus on promoting neurotypical behaviours rather than the priorities of autistic people.

Implementation of Nanopore sequencing as a pragmatic workflow for copy number variant confirmation in the clinic.

BACKGROUND: Diagnosis of rare genetic diseases can be a long, expensive and complex process, involving an array of tests in the hope of obtaining an actionable result. Long-read sequencing platforms offer the opportunity to make definitive molecular diagnoses using a single assay capable of detecting variants, characterizing methylation patterns, resolving complex rearrangements, and assigning findings to long-range haplotypes. Here, we demonstrate the clinical utility of Nanopore long-read sequencing by validating a confirmatory test for copy number variants (CNVs) in neurodevelopmental disorders and illustrate the broader applications of this platform to assess genomic features with significant clinical implications. METHODS: We used adaptive sampling on the Oxford Nanopore platform to sequence 25 genomic DNA samples and 5 blood samples collected from patients with known or false-positive copy number changes originally detected using short-read sequencing. Across the 30 samples (a total of 50 with replicates), we assayed 35 known unique CNVs (a total of 55 with replicates) and one false-positive CNV, ranging in size from 40 kb to 155 Mb, and assessed the presence or absence of suspected CNVs using normalized read depth. RESULTS: Across 50 samples (including replicates) sequenced on individual MinION flow cells, we achieved an average on-target mean depth of 9.5X and an average on-target read length of 4805 bp. Using a custom read depth-based analysis, we successfully confirmed the presence of all 55 known CNVs (including replicates) and the absence of one false-positive CNV. Using the same CNV-targeted data, we compared genotypes of single nucleotide variant loci to verify that no sample mix-ups occurred between assays. For one case, we also used methylation detection and phasing to investigate the parental origin of a 15q11.2-q13 duplication with implications for clinical prognosis. CONCLUSIONS: We present an assay that efficiently targets genomic regions to confirm clinically relevant CNVs with a concordance rate of 100%. Furthermore, we demonstrate how integration of genotype, methylation, and phasing data from the Nanopore sequencing platform can potentially simplify and shorten the diagnostic odyssey.

Intestinal transit amplifying cells require METTL3 for growth factor signaling, KRAS expression, and cell survival.

Intestinal epithelial transit amplifying cells are essential stem progenitors required for intestinal homeostasis, but their rapid proliferation renders them vulnerable to DNA damage from radiation and chemotherapy. Despite their critical roles in intestinal homeostasis and disease, few studies have described genes that are essential to transit amplifying cell function. We report that the RNA methyltransferase, METTL3, is required for survival of transit amplifying cells in the murine small intestine. Transit amplifying cell death after METTL3 deletion was associated with crypt and villus atrophy, loss of absorptive enterocytes, and uniform wasting and death in METTL3-depleted mice. Ribosome profiling and sequencing of methylated RNAs in enteroids and in vivo demonstrated decreased translation of hundreds of unique methylated transcripts after METTL3 deletion, particularly transcripts involved in growth factor signal transduction such as Kras. Further investigation confirmed a novel relationship between METTL3 and Kras methylation and protein levels in vivo. Our study identifies METTL3 as an essential factor supporting the homeostasis of small intestinal tissue via direct maintenance of transit amplifying cell survival. We highlight the crucial role of RNA modifications in regulating growth factor signaling in the intestine, with important implications for both homeostatic tissue renewal and epithelial regeneration.

NADcapPro and circNC: methods for accurate profiling of NAD and non-canonical RNA caps in eukaryotes.

Accurate identification of NAD-capped RNAs is essential for delineating their generation and biological function. Previous transcriptome-wide methods used to classify NAD-capped RNAs in eukaryotes contain inherent limitations that have hindered the accurate identification of NAD caps from eukaryotic RNAs. In this study, we introduce two orthogonal methods to identify NAD-capped RNAs more precisely. The first, NADcapPro, uses copper-free click chemistry and the second is an intramolecular ligation-based RNA circularization, circNC. Together, these methods resolve the limitations of previous methods and allowed us to discover unforeseen features of NAD-capped RNAs in budding yeast. Contrary to previous reports, we find that 1) cellular NAD-RNAs can be full-length and polyadenylated transcripts, 2) transcription start sites for NAD-capped and canonical m7G-capped RNAs can be different, and 3) NAD caps can be added subsequent to transcription initiation. Moreover, we uncovered a dichotomy of NAD-RNAs in translation where they are detected with mitochondrial ribosomes but minimally on cytoplasmic ribosomes indicating their propensity to be translated in mitochondria.

Linking genotypic and phenotypic changes in the E. coli Long-Term Evolution Experiment using metabolomics.

Changes in an organism's environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism's metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the Long-Term Evolution Experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass-spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.

Integration of a Cross-Ancestry Polygenic Model With Clinical Risk Factors Improves Breast Cancer Risk Stratification.

PURPOSE: To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups. METHODS: We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic. RESULTS: The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone. CONCLUSION: Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.

The hypoxia response pathway promotes PEP carboxykinase and gluconeogenesis in C. elegans.

Actively dividing cells, including some cancers, rely on aerobic glycolysis rather than oxidative phosphorylation to generate energy, a phenomenon termed the Warburg effect. Constitutive activation of the Hypoxia Inducible Factor (HIF-1), a transcription factor known for mediating an adaptive response to oxygen deprivation (hypoxia), is a hallmark of the Warburg effect. HIF-1 is thought to promote glycolysis and suppress oxidative phosphorylation. Here, we instead show that HIF-1 can promote gluconeogenesis. Using a multiomics approach, we reveal the genomic, transcriptomic, and metabolomic landscapes regulated by constitutively active HIF-1 in C. elegans. We use RNA-seq and ChIP-seq under aerobic conditions to analyze mutants lacking EGL-9, a key negative regulator of HIF-1. We integrate these approaches to identify over two hundred genes directly and functionally upregulated by HIF-1, including the PEP carboxykinase PCK-1, a rate-limiting mediator of gluconeogenesis. This activation of PCK-1 by HIF-1 promotes survival in response to both oxidative and hypoxic stress. Our work identifies functional direct targets of HIF-1 in vivo, comprehensively describing the metabolome induced by HIF-1 activation in an organism.

IMP1/IGF2BP1 in human colorectal cancer extracellular vesicles.

Colorectal cancer (CRC) is a leading cause of cancer-related death. There is an urgent need for new methods of early CRC detection and monitoring to improve patient outcomes. Extracellular vesicles (EVs) are secreted, lipid-bilayer bound, nanoparticles that carry biological cargo throughout the body and in turn exhibit cancer-related biomarker potential. RNA binding proteins (RBPs) are posttranscriptional regulators of gene expression that may provide a link between host cell gene expression and EV phenotypes. Insulin-like growth factor 2 RNA binding protein 1 (IGF2BP1/IMP1) is an RBP that is highly expressed in CRC with higher levels of expression correlating with poor prognosis. IMP1 binds and potently regulates tumor-associated transcripts that may impact CRC EV phenotypes. Our objective was to test whether IMP1 expression levels impact EV secretion and/or cargo. We used RNA sequencing, in vitro CRC cell lines, ex vivo colonoid models, and xenograft mice to test the hypothesis that IMP1 influences EV secretion and/or cargo in human CRC. Our data demonstrate that IMP1 modulates the RNA expression of transcripts associated with extracellular vesicle pathway regulation, but it has no effect on EV secretion levels in vitro or in vivo. Rather, IMP1 appears to affect EV regulation by directly entering EVs in a transformation-dependent manner. These findings suggest that IMP1 has the ability to shape EV cargo in human CRC, which could serve as a diagnostic/prognostic circulating tumor biomarker.NEW & NOTEWORTHY This work demonstrates that the RNA binding protein IGF2BP1/IMP1 alters the transcript profile of colorectal cancer cell (CRC) mRNAs from extracellular vesicle (EV) pathways. IMP1 does not alter EV production or secretion in vitro or in vivo, but rather enters CRC cells where it may further impact EV cargo. Our work shows that IMP1 has the ability to shape EV cargo in human CRC, which could serve as a diagnostic/prognostic circulating tumor biomarker.

The landscape of transcriptional and translational changes over 22 years of bacterial adaptation.

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here, we use the Escherichia coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

The reason we look like our parents is because we inherit their genes. Genes carry the instructions for our cells to make messenger RNAs (mRNAs), which our cells then translate into proteins. Proteins, in turn, determine many of our features. This is true for all living organisms. Any changes ­ or mutations ­ in an organism's genes can lead to variations in its proteins, which can alter the organism's traits. This is the basis for evolution: mutations can lead to changes that allow an organism to better adapt to a new environment. This increases the organism's chances of survival and reproduction ­ its evolutionary 'fitness' ­ and makes it more likely that the mutation that generated the new trait in the first place will be passed on to the organism's descendants. However, just because two organisms have evolved similar traits to adapt to similar environments, it does not mean that the genetic basis for the adaptation is the same. For example, many animals share similar coloring to warn off predators, but the way that coloring is coded genetically is completely different. In species that are related (which share many of the same genes), this type of evolution is called 'parallel evolution', and it can make it difficult for scientists to understand how an organism evolved and pinpoint exactly what mutations are linked to which features. In 1988, scientists established the 'long-term evolution experiment' to tackle questions about how evolution works. The experiment, which has been running for over 30 years, consisted on tracking the evolution of 12 populations of Escherichia coli bacteria grown in separate flasks containing the same low-nutrient medium. The initial 12 populations were genetically identical, making this an ideal system to study parallel evolution, since all the populations had to evolve to adapt to the same environment, whilst isolated from each other. In previous experiments, scientists had already noted that while the different bacterial populations grew in similar ways, they had mostly different mutations. To better understand parallel evolution, Favate et al. analyzed the synthesis rates of RNA and proteins in the E. coli populations used in the long-term evolution experiment. They found that 22 years after the start of the experiment, all 12 populations produced more RNA, grew faster and were bigger. Additionally, while the different populations had accumulated few shared mutations after 22 years, they all shared similar patterns of RNA levels and protein synthesis rates. Further probing revealed that parallel evolution may be linked to how genes are regulated: mutations in regulators of related groups of genes involved in the same processes inside the cell can amplify the degree of parallel changes in organisms. This means that mutations in these genes may lead to similar traits. These findings provide insight into how parallel evolution arises in the long-term evolution experiment, and provides clues as to how the same traits can evolve several times.

Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage.

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.

Isolation, profiling, and tracking of extracellular vesicle cargo in Caenorhabditis elegans.

Extracellular vesicles (EVs) may mediate intercellular communication by carrying protein and RNA cargo. The composition, biology, and roles of EVs in physiology and pathology have been primarily studied in the context of biofluids and in cultured mammalian cells. The experimental tractability of C. elegans makes for a powerful in vivo animal system to identify and study EV cargo from its cellular source. We developed an innovative method to label, track, and profile EVs using genetically encoded, fluorescent-tagged EV cargo and conducted a large-scale isolation and proteomic profiling. Nucleic acid binding proteins (∼200) are overrepresented in our dataset. By integrating our EV proteomic dataset with single-cell transcriptomic data, we identified and validated ciliary EV cargo: CD9-like tetraspanin (TSP-6), ectonucleotide pyrophosphatase/phosphodiesterase (ENPP-1), minichromosome maintenance protein (MCM-3), and double-stranded RNA transporter SID-2. C. elegans EVs also harbor RNA, suggesting that EVs may play a role in extracellular RNA-based communication.

Neurodevelopmental disorders and neurodiversity: definition of terms from Scotland's National Autism Implementation Team.

Adults with neurodevelopmental disorders frequently present to, but fit uneasily into, adult mental health services. We offer definitions of important terms related to neurodevelopmental disorders through unifying research data, medical and other viewpoints. This may improve understanding, clinical practice and development of neurodevelopmental disorder pathways within adult mental health services.

riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow.

MOTIVATION: Ribosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. RESULTS: We present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system for end-to-end processing of Ribo-seq data. riboviz 2 has been extensively tested on diverse species and library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline. AVAILABILITY AND IMPLEMENTATION: riboviz 2 is freely available at github.com/riboviz/riboviz.

Exploring Ribosome-Positioning on Translating Transcripts with Ribosome Profiling.

The emergence of ribosome profiling as a tool for measuring the translatome has provided researchers with valuable insights into the post-transcriptional regulation of gene expression. Despite the biological insights and technical improvements made since the technique was initially described by Ingolia et al. (Science 324(5924):218-223, 2009), ribosome profiling measurements and subsequent data analysis remain challenging. Here, we describe our lab's protocol for performing ribosome profiling in bacteria, yeast, and mammalian cells. This protocol has integrated elements from three published ribosome profiling methods. In addition, we describe a tool called RiboViz (Carja et al., BMC Bioinformatics 18:461, 2017) ( https://github.com/riboviz/riboviz ) for the analysis and visualization of ribosome profiling data. Given raw sequencing reads and transcriptome information (e.g., FASTA, GFF) for a species, RiboViz performs the necessary pre-processing and mapping of the raw sequencing reads. RiboViz also provides the user with various quality control visualizations.