dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicates that the RNA structure may also help maintaining the flux of ribosomes along the ORF to minimize ribosome collisions, as proposed in a previous study  (34). Interestingly, we were able to confirm the positive influence of RNA secondary structure using dimethyl sulfate-sequencing-based measurements of secondary structure density (50) rather than computational predictions; despite the experimental dataset being sparser than our computational predictions (only 231 ORFs for which we had protein synthesis and ribosome density data also had experimental data on secondary structure), the density of secondary structure (measured by a Gini index; see ref. 50  and Dataset S8) correlated positively with our SDRs (Pearson correlation coefficient = 0. 17; P = 0. 007). Our results do not exclude "controlled" ribosome stalling at specific positions, such as on upstream ORFs  (51) , or at codons for which cognate tRNAs are limiting in specific conditions, where active regulatory mechanisms are used to modulate the output of specific ORFs  (49).
Materials and Methods Simulations. All simulations of the TASEP model have been performed with C++ code developed in-house and available in the github repository at the following link: https://github. com/andreariba/codon_TASEP. The size of the ribosome footprint has been set to 10 codons. Analysis of Poly-A Selected RNA-Seq. Reads from the fastq files associated with the publication of ref. 17 have been trimmed with cutadapt (59) with parameters "-error-rate 0. 1 -minimum-length 15 -overlap 1," first of the 3′ adapter (TGGATTCTCGGGTGCCAAGG) and then of the poly-A tail ["adapter" = (A) 50 ]. Resulting sequences of at least 15 nt were then mapped to yeast ORFs obtained from the yeastgenome. org (60) database (https:// downloads. yeastgenome. org/sequence/S288C_reference/orf_dna/), with the bowtie2 (61) aligner, version 2.
2. 9, with parameter "-q" (fastq format). For each read, the best alignment reported by bowtie2 was used, and expression levels for each ORF, expressed in reads per kilobase per million (RPKM) were calculated by dividing the number of reads mapped to the ORF by library size and ORF length, then multiplying by 10 6. For each ORF, the expression level used in the analysis was the average computed from 3 replicates. Analysis of Ribosome Profiling Data. Ribo-seq data have been downloaded from the Gene Expression Omnibus database (62) (accession number GSE53313). Reads from raw fastq files were trimmed with cutadapt (3′ adapter: TCGTATGCCGTCTTCTGCTTG) with parameters "-error-rate 0. 1 -minimum-length 15 -overlap 1. " The first 8 nt corresponding to random barcodes were then trimmed as well and the remainder of the sequence was first aligned to rRNAs with bowtie2, version 2.
2. 9, parameters "-q" and "-un" to indicate the fastq format of the input file and to obtain also the unmapped reads. The latter were then aligned to a database consisting of all yeast ORFs extended by 200 nt upstream and downstream, to be able to reconstruct full-length ribosome profiles along the ORFs. To allow for the possibility of closely spaced ORFs which would lead to reads mapping in an overlapping manner to the 2 genes, we extracted up to 2 best mappings, with bowtie2 parameter "-k 2. " The positions of the "A" site of ribosomes in individual reads were inferred as in ref. 4. Codon densities have been estimated as in ref. 4 by considering only ORFs with more than 1 read per codon and removing the first 200 codons. For each ORF, deviations were computed relative to the average number of reads per codon.
4 m/z, and 1 microscan was acquired for each spectrum. The acquired raw files were imported into the Progenesis QI software (version 2. 0; Nonlinear Dynamics, Limited), which was used to extract peptide precursor ion intensities across all samples applying the default parameters. The generated mgf files were searched using MASCOT using the following search criteria: full tryptic specificity was required (cleavage after lysine or arginine residues, unless followed by proline); 3 missed cleavages were allowed; carbamidomethylation (C) was set as fixed modification; oxidation (M) and heavy SILAC (K8) were applied as variable modifications; mass tolerance of 10 ppm (precursor) and 0. 02 Da (fragments). The database search results were filtered using the ion score to set the FDR to 1% on the peptide and protein level, respectively, based on the number of reverse protein sequence hits in the datasets.
The relative quantitative data obtained were normalized and statistically analyzed using our in-house script (SafeQuant) as above  (63). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (64) partner repository with the dataset identifier PXD014357. The protein synthesis rates have been obtained from the slope of linear regression constrained to 0 at time point 0. This analysis was performed with the lm() function of R (version 3. 4. 2). For each regression, R 2 values have been recorded for further analysis (Fig. 2C ). Scaling mRNA Copy Numbers, Protein Synthesis Rates, and Ribosome Densities. To obtain absolute protein synthesis rates, the relative rates obtained from pSILAC were scaled, using the known values of the number of protein molecules per yeast cell  (21) , the doubling time of yeast, and the average half-life of yeast proteins  (22).
Absolute abundances of mRNA molecules per cell were obtained by rescaling the relative numbers inferred from RNA-seq to obtain a total of 40,000 transcripts per cell, as found in previous work  (23, 24). Computation of mRNA/Protein Features. Protein features used in the analysis of translation elongation rate have been downloaded from the yeast genome database at the following link: https://downloads. yeastgenome. org/curation/ calculated_protein_info/protein_properties. tab. The tRNA adaptation index (tAI) and normalized tAI (ntAI) have been computed as in refs. 26 and 27 with a custom Python script. The RNAplfold tool from ViennaRNA package (65) (version 2. 1. 8) was used with default parameters to estimate structural accessibility along ORFs. For each ORF, the average accessibility of windows of a specified size has been calculated.
2011 ) and ending with ACMG-compliant classification. PeCanPIE offers three key functions: (1) automated annotation, classification, and triage via our MedalCeremony pipeline  (Zhang et al. 2015) ; (2) an interactive variant page and visualization tools to support expert curation and committee review; and (3) a reference database of expert-reviewed germline cancer-predisposing mutations. PeCanPIE is designed on the theme of genetic variant analysis, making it flexible for extending its application to noncancer-related genetic diseases.
Alternatively, users may specify their own candidate gene list for analysis. Variants are next assigned gene and protein annotations with the Ensembl Variant Effect Predictor (VEP) pipeline  (McLaren et al. 2016 ) and filtered by functional class and population frequency derived from the Exome Aggregation Consortium (ExAC) database  (Lek et al. 2016). To ensure that pathogenic germline variants in cancer patients are retained, PeCanPIE uses the non-TCGA subset of ExAC that excludes patient samples from The Cancer Genome Atlas (TCGA)  (The Cancer Genome Atlas Research Network 2008). The remaining variants are stratified into three tiers (gold, silver, and bronze) as an indication of potential pathogenicity computed by our MedalCeremony pipeline (see below). Finally, each "medaled" variant is linked to a stand-alone page featuring an interface to support semiautomated pathogenicity classification using ACMG guidelines.
2014). Variants appearing at a frequency higher than the cutoff may still be classified if they are present either in the International Agency for Research on Cancer (IARC) TP53 database or in ClinVar with a classification of pathogenic or likely pathogenic and a review status of two or more gold stars (https://www. ncbi. nlm. nih. gov/clinvar/docs/review_status/). The latter exception helps retain pathogenic variants that may appear at higher population frequencies owing to founder effects, partial penetrance, or other factors impacting phenotypic heterogeneity and age at onset. To support incorporation of custom data for variant classification, users may optionally provide a "whitelist" of custom variants, matches to which will always receive a medal, or a "blacklist," matches to which will be prevented from receiving a medal.
Additional annotations are incorporated to aid with the classification process: (1) COSMIC  (Forbes et al. 2008)   (Downing et al. 2012). Although COSMIC and PCGP represent somatic rather than germline data sets, these can help inform germline classification; an example is discussed below in the "ACMG classification interface" section. An overview of the gold, silver, and bronze classification scheme implemented in MedalCeremony is shown in Figure  2. Gold medals are assigned to truncating variants (including splice variants) in genes where loss-of-function variants are associated with a disease (e. g. , tumor suppressor genes in cancer)  (Zhao et al. 2016; Chakravarty et al. 2017)  as well as matches to highly curated databases, including IARC TP53  (Bouaoun et al. 2016) , ClinVar pathogenic (P) or likely pathogenic (LP) variants with a review status of two or more gold stars, Arizona State University (ASU) TERT  (Podlevsky et al.
2007) , the University of Utah Department of Pathology (ARUP) MEN2  (Margraf et al. 2009) , and the National Human Genome Research Institute (NHGRI) Breast Cancer Information Core  (Szabo et al. 2000). Gold medals are also assigned to somatic mutation hotspots in COSMIC (observed in ≥10 tumors after removal of hypermutators) and PCGP, St. Jude committee-reviewed germline P/LP variants, and user-provided whitelist variants, if specified. Silver medals are assigned to in-frame indels, truncation events in non-tumor suppressor genes, variants predicted to be damaging by in silico algorithms, and matches to additional databases: ClinVar P/LP with fewer than two gold stars, BRCA Share  (Béroud et al. 2016) , ALSoD  (Abel et al. 2013) , Leiden Open Variation Database (LOVD)  (Fokkema et al. 2011 ) locus-specific databases for APC and MSH2, and RB1  (Lohmann and Gallie 1993).
Unless otherwise medaled, variants predicted to be tolerated by in silico algorithms are assigned a bronze medal. Imperfect database matches (e. g. , a different allele at the same genomic position or at the same codon but with a different amino acid change) are typically assigned a lower-grade medal, for example, silver rather than gold. Variants not meeting any of the previous criteria, for example, most silent variants and those without any functional annotations, will not receive a medal. Amino acid and pathogenicity codes from the diverse variant databases used in this process are standardized to improve the reliability of annotations and utility of information (Methods). A summary of resources is shown in Table  1. MedalCeremony may also be run as a stand-alone pipeline on St. Jude Cloud (Methods).
Variant review interface After MedalCeremony classification, the results are presented in a table that can be searched or filtered by gene, variant class, medal, or expert review classification (Fig. 3A ). If a variant has been previously classified by the user or the St. Jude germline variant review committee, that information will be prepopulated. Variant classifications previously performed by each user are also prepopulated, allowing groups working on other diseases to establish their own expert review committees (see "Variant classification of noncancer genes" below). Each row links to a variant page containing extensive annotations, including gene information from the National Truncating variants in loss-of-function genes (e. g. , tumor suppressors) and those matching highly curated databases receive gold medals.
Truncations in non-loss-of-function genes, in-frame indels, variants predicted to be damaging, and matches to additional databases receive silver medals. Otherwise, variants predicted to be tolerated by damage-prediction algorithms receive bronze medals. Imperfect database matches receive a lower-grade medal than exact matches. Variants not meeting any of the prior criteria are labeled "unknown. " Center for Biotechnology Information (NCBI) and the Online Mendelian Inheritance in Man (OMIM) database  (Amberger et al. 2015) , ClinVar match details, and population frequencies (Fig. 3B ). Cancer predisposition genes are classified as autosomal dominant and autosomal recessive  (Zhang et al. 2015)  and annotated as such on the interface; this is used for determining the status of a patient but not during automated classification of individual variants.
For TP53, data from several variant-level databases of functional activity  (Soussi et al. 2006; Giacomelli et al. 2018; Kotler et al. 2018 ) are presented to aid reviewers in assessing pathogenicity. Examples of pathogenic and benign variants are shown in Figure  3D ; additional examples, including those classified as variant of uncertain significance (VUS) by our analysis (e. g. , TP53 P222L), can be explored on our portal (https://pecan. stjude. cloud/variant/67664). In addition to TP53, we have also incorporated published functional data from IKZF1  (Churchman et al. 2018 ) and will continue to expand the repertoire of functional data generated by basic research scientists.
0001) and the companion BA1 tag (>5% population frequency in ExAC non-TCGA), PM4 (in-frame protein insertions and deletions), PS1, and PM5 (amino acid comparisons made vs. pathogenic variants in ClinVar or those identified by the St. Jude germline variant review committee). Automatically assigned tags may be removed by the analyst if desired. This automation provides improved support over manual curation interfaces while still retaining analyst control over the ultimate classification decisions. As shown on the variant page for ETV6 Arg359Ter, the single gold medal variant detected in the patient with ALL was expert-classified as likely pathogenic because the mutation is present in a disease-related gene (i. e. , ETV6 is a pediatric ALL driver gene), is a loss-of-function null variant, and is not present in the ExAC non-TCGA database (Fig.
4 ), consistent with the tumor suppressor gene model. This pathogenic variant was discovered in a research project, in which the MedalCeremony pipeline flagged this variant repeatedly in an affected family. Reviewers may enter custom evidence such as this into the interface for use during final classification. Recurrent mutations, which include many gain-of-function variants, are reported either through matches to somatic mutation hotspots or to pathogenic variants in curated germline databases, such as ClinVar. Figure  5  shows a germline NRAS G12S variant detected in one patient to illustrate this case. Somatic data rendered in the ProteinPaint view shows a hotspot at G12, and automated classification detects pathogenic variations at the same amino acid position in ClinVar, as well as a hotspot in COSMIC data.
Variant classification of noncancer genes Using the collaborative features described above, we are in the process of classifying and reviewing variants with the Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. To illustrate the utility of PeCanPIE to disease areas outside the realm of cancer genomics, we show the classification of a variant in the superoxide dismutase 1 (SOD1) gene reported by the ALS research community in the ALSoD  database. Following ACMG classification criteria and taking into account the disease model as reported in OMIM, researchers at St. Jude are performing initial classifications, which undergo subsequent review by external researchers via secure remote login. As an example, we show a gold standard variant Ala5Val in the superoxide dismutase 1 (SOD1) gene (Fig.
Use of PeCanPIE by the genetic research community PeCanPIE was initially designed in support of large-scale germline variation analysis projects and was iteratively improved based on the feedback of an interdisciplinary group of researchers and individual users. Germline variants from the following studies have been analyzed thus far: (1) A study of germline variations in predisposition genes in 1120 children with cancer  (Zhang et al. 2015)  classified 890 variants, identifying 109 as pathogenic (P) and 25 as likely pathogenic (LP); (2) the St. Jude LIFE project, a follow-up study of 3006 long-term survivors of pediatric cancer  (Wang et al. 2018) , classified 3417 variants, including 188 P and 160 LP, for assessing genetic risk of secondary neoplasms in adulthood; and (3) the Genomes for Kids clinical research study of pediatric cancer patients (https://clinicaltrials.
gov/ct2/show/ NCT02530658), which is ongoing. The expert-curated decisions for the first two published studies were also reapplied to incoming variant classification requests. We have also engaged the CReATe Consortium to expand PeCanPIE's application for noncancer studies. Variants in 522 ALS patients from CReATe are being curated for upload to PeCanPIE. A future direction by this working group is the inclusion of tracks displaying intrinsically disordered regions (IDRs), a hallmark of known ALS pathogenic genes, which will allow for further prioritization of novel variants. In addition to large-scale genetic variation analysis, PeCanPIE also supports individual users who may have a small number of patient samples. Currently there are 228 registered individual users involved in a variety of human genetic studies (e.
Discussion Although PeCanPIE's features partially overlap those of other available tools, we know of no other variant analysis system offering end-to-end processing with the same level of functionality. InterVar  (Li and Wang 2017)  provides some similar features, however its web-based implementation can analyze only a single variant at a time and only SNVs; it cannot handle indels, MNVs, or complex substitutions, nor can it batch-process variants. The offline version is more capable; however, it relies on the semicommercial ANNOVAR software  (Wang et al. 2010) , which requires paid licensing even for nonprofit organizations. CIViC  (Griffith et al. 2017 ) offers a detailed curation model, but for predisposing variants it simply directs users to manually follow the ACMG classification guidelines.
The ClinGen Pathogenicity Calculator  (Patel et al. 2017 ) offers a comprehensive ACMG classification interface, but only works with a single variant at a time, lacks automation, and cannot batch-process variants. On the other end of the spectrum, CRAVAT  (Masica et al. 2017 ) can batch-process collections of variants, but pathogenicity is ranked based on machine learning-based VEST and CHASM scores  (Carter et al. 2009 (Carter et al. , 2013)) ; in contrast, PeCanPIE provides more granular annotations and discrete ACMG-recommended evidence tags, which allow analysts to see and weigh these individual contributions to overall variant pathogenicity. PeCanPIE also offers additional capabilities. Novel features include (1) tight integration of variant classification with the rich resource of somatic mutation data in pediatric cancer, which can be explored online via the embedded ProteinPaint view; (2) enhancement to splice variant annotation (Methods); (3) to aid analysts with literature review, PubMed references are extracted from ClinVar, VEP, COSMIC, and locus-specific databases and hyperlinked; (4) collaborative features allow users to invite others to share results or work together on classifying sets of variants; and (5) cloud-based implementation of PeCanPIE, which obviates the need for complex software installation and command-line workflows.
This design also allows back-end analysis pipelines to be invoked independently from PeCanPIE for users who prefer direct or programmatic access over a graphical interface. A limitation of the existing method is that precomputed damage-prediction algorithm scores are taken from the dbNSFP database, which only contains data for nonsilent SNVs. Although these annotations are unavailable for indels, because protein class annotations are taken into account by the scoring algorithm, highimpact events such as truncating variations will still be highly ranked. For variant population frequency filtering, we are currently using the non-TCGA subset of ExAC instead of gnomAD  (Lek et al. 2016 ) because the gnomAD database contains TCGA samples; we plan to migrate to gnomAD once a TCGA-subtracted version becomes publicly available.
Inaccuracy in public databases, such as misannotation of a rare germline variant as a somatic hotspot mutation, may lead to an over-rated gold medal assigned by MedalCeremony, which will require literature review for final pathogenicity classification. In conclusion, the PeCanPIE platform significantly accelerates the variant classification process by automating many prerequisite steps, helping to prioritize potentially pathogenic variants in massively parallel sequencing data, and providing a robust platform for investigating variant pathogenicity in disease-related genes. Although PeCanPIE was developed and tested with pediatric cancer susceptibility as the initial focus, its use is now being expanded to other pediatric and adult diseases. Users are now able to specify custom gene lists to analyze appropriately to their diseases of interest, enabling disease-specific variant curation, and facilitating gene discovery.
Somatic medals Although the PeCanPIE platform is intended to support only classification of germline variants, a basic somatic medal call is also provided to assist manual curation by considering the variant in a somatically acquired context. The somatic medal is primarily based on direct matches to somatic mutation hotspots or loss-offunction mutations in tumor suppressor genes annotated in the two somatic databases incorporated in this study, that is, PCGP and COSMIC. The separate logic can result in different medals assigned to somatic and germline. For example, the NOTCH1 R1350L variant in Figure  3  receives a bronze somatic medal and a germline silver medal; the more significant germline medal is attributable to a match in the ClinVar database, a resource that is not considered by the somatic classifier.
St. Jude Cloud Although PeCanPIE was designed as a web portal to maximize ease of use for non-bioinformaticians, two component pipelines are also publicly accessible. On its back-end, St. Jude Cloud (https ://stjude. cloud) uses DNAnexus (https://www. dnanexus. com/), a platform in which user-created software pipelines can be installed and run on cloud computing instances. A DNAnexus account is required to use PeCanPIE for secure storage and to send notifications when submitted jobs are complete. Once a pipeline has been installed on DNAnexus, it is straightforward for nonexpert users to run it, either from a standardized web interface or a commandline client. We have installed two pipelines used by PeCanPIE on DNAnexus: VEP+ for variant annotation (app-stjude_vep_plus) and MedalCeremony for automated classification (app-stjude_me-dal_ceremony).
These pipelines provide a wide variety of annotations which are then displayed by the PeCanPIE portal; bioinformaticians or others who would prefer direct access to the annotations can access them via the pipelines in a simple tab-delimited text format. The availability of these component pipelines on the cloud provides users and institutions straightforward, scalable access to the software, and our centralized maintenance allows all users to immediately benefit from updates and new features as they become available. An example workflow using the DNAnexus command-line client can be found in Supplemental Methods. For users who prefer not to use command-line tools, DNAnexus also provides a graphical interface for configuring and running the cloud pipelines. PeCanPIE is free for noncommercial use.