dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Figure  2A , fragment constructs have been used in more studies than mutations, although some of these might have been designed for convenience reasons (e. g. constructs that are easier to express in heterologous systems). Interactive network representations highlighting mutations and binding regions can be found in https://doi. org/10. 18119/N9W590 and in https://doi. org/10. 18119/N90W3W. Regarding interaction data generation approaches, the experimental setup used to detect an interaction is summarized by three key fields in the IMEx curation model: the method used to determine that an interaction is happening ('interaction detection method'), the method used to determine which molecules are involved ('participant detection method') and the biological environment in which the interaction occurs ('host organism').
Each of these is described by an appropriate controlled vocabulary term, making the data readily searchable. In the coronavirus dataset, the overwhelming majority of data were generated by AP-MS approaches performed in A549, HEK293(T) or HeLa cells (Supplementary Table  1B ). This type of data produces sets of potential interacting partners (preys) associated with a bait of interest but does not directly identify binary interacting partners. Also, the data needs to be automatically expanded into binaries for its representation in tabular or network graph form. This accounts for a large proportion of binary interactions from spoke-expanded n-ary relationships found in the dataset: 3267 out of 4477 interactions (73%) are expanded n-ary relationships, while only 41% of the binary interactions found in the full IntAct database are expanded n-ary relationships.
The extreme urgency in the study of SARS-CoV-2 has resulted in a strong dominance of HT data for this species in comparison with the other members of Coronaviridae reported in the dataset (Figure  3A ). Additionally, SARS-CoV-2 smallscale studies are mainly focused around the ACE2-Spike interaction due to its relevance for virion recognition and infection. For the remaining interactions, most SARS-CoV-2 data comes from three studies: Stukalov et al. (25) , where A549 cell lines were transduced with viral proteins followed by AP-MS analysis, and Gordon et al. (24)  and Li et al. (26) , both focused on AP-MS detection of interacting candidates in HEK293(T) cells transfected with SARS-CoV-2 proteins. The studies are distinct, showing small overlap (Figure  3B-D ) with each other and no overlap at all with small-scale studies.
They also show different degree distribution patterns (Figure  3E ). Gordon et al. shows an unexpected pattern of fully isolated components, likely due to stringent target selection. Lack of overlap between different HT interaction datasets is a long-recognized phenomenon, since different experimental approaches are better suited to detect interactions featuring specific physicochemical characteristics and protein abundances, among other parameters  (27) (28) (29). Even in this case, where the approach used is broadly similar, they likely reflect methodological differences. AP-MS datasets are very sensitive to protein abundances and affinities, as well as to the selection and orientation of tags and expression systems. Additionally, strong differences can arise during the selection of bona fide interactors, where multiple strategies can be used in order to clean up spurious detections.
The lack of common interactions between the different studies focused on SARS-CoV-2 suggests that more of these systematic experiments are needed to increase reliability of biological conclusions extracted from this type of data. Exploring the biological context of SARS-CoV-2 interactors suggests that the three HT studies are complementary. Pathway enrichment (Figure  4  and Supplementary Table  2 ) finds commonalities on expected pathways related with cell cycle, response to stress and infectious disease, and molecules synthesis and processing. Key pathways such as innate immune response or cytokine signaling are only found in the dataset of Li et al. , along with several intracellular signaling and metabolic routes. Only SARS-CoV-1 shows enrichment in lung tissue, but none of the sets reach the 5× enrichment threshold suggested by Jain and Tuteja (17) (Supplementary Figure  1 , Supplementary Table  3 ).
The functionality and/or stability of these proteins are further increased through the formation of protein complexes, all of which have been catalogued into the Complex Portal. To date, 14 complexes have been identified in each strain, formed by viral-viral protein interactions, including homomeric assemblies such as the dimeric SARS-CoV-2 main protease complex (CPX-5685). Other reference entities such as the SARS-CoV-2 Spike-human ACE2 receptor complex (CPX-5683) have also been created to enable their identification in large omics datasets. All complexes have been annotated with Gene Ontology terms, describing their role in the virus lifecycle, which again will assist in the analysis of large omics-derived datasets.
Conclusions Accurate and detailed representation of biological insight into public databases is a fundamental source of data for scientific discovery, even more so in a situation of accelerated research work such as the current pandemic. Our curation of molecular interactions related to Coronaviridae enables a systematic perspective of this data and greatly increases its interoperability. From our overview analysis of the data available so far, we can highlight how the SARS-CoV-2 data seem to be both biologically relevant, and thus informative for research on the disease, and strongly preliminary, so full consideration for its inherent incompleteness should be given when using it. The IMEx Consortium is expanding the resource as new data become available, striving to provide the most accurate possible picture of the Coronaviridae interactome.
al. , protein production, Ca 2+ -dependent cell signaling and cell cycle control are mostly represented in the dataset provided by Li et al. while proteins involved in transcriptional regulation and transport have been found in the interactome provided by Stukalov and collaborators. Development of the IMEx coronavirus dataset complements other curation efforts that have been initiated in the light of the pandemic. Interaction resources BioGRID (31) and VirHostNet (the latter specialized in virus-host interaction data) (32) have produced shallow curation interaction datasets taken from COVID-19-related literature. The IMEx Coronavirus dataset has been used by the DisGeNET database (33) for contextual annotation with related disea ses (https://www. disgenet. org/downloads#section9). Also, members of the IMEx Consortium are involved in the COVID-19 Disease Map initiative (https://covid.
Background: Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. Results: Here we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed > 70% concordance with fluorescence in situ hybridization (FISH) results.
Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches.
g. a single copy gain leads to VAFs shifting towards 1/3 or 2/3). Since multiple germline SNPs typically coexist in a copy number altered region, their VAF information when used together as a group provides more signal for detecting CNVs than individual mutations. This information not only facilitates the estimation of copy numbers but also allows for the identification of CN-LOH and hyperdiploidy events that are commonly observed in cancer. A comparison of the advantages and disadvantages of read depth-versus VAFbased approaches is outlined in Table  1. To illustrate the performance of CNV Radar in realworld scenarios, we evaluated it on several large-scale cancer datasets including multiple myeloma, AML, and prostate cancer, and compared the exome-derived CNVs with CNVs defined by FISH, WGS, and microarray assays.
Evaluation of CNV Radar on the AML and prostate TCGA datasets In addition to the MMRF CoMMpass study, we examined the performance of CNV Radar on two TCGA Fig. 2  Schematic diagram for assigning copy number types to each genomic segment that was defined by identified breakpoints. mCNV, median copy number variation; CNV, copy number variation; mLFC, median log 2 fold change; LFC, log 2 fold change; mVAF, median variant allele frequency; VAF, variant allele frequency; LOH, loss of heterozygosity datasets (AML and prostate)  [8, 36]  and compared the CNV Radar calls to CNVs derived from SNP6 arrays in each study. We also used these datasets to evaluate the performance of several other CNV callers: CNVkit, CoNIFER, CopywriteR, and ExomeDepth. In both TCGA AML (Fig. 7 , Table  4 ) and prostate (Fig.
Conclusion The field of oncology drug development is growing increasingly dependent on the identification of biomarkers for drug approval  [44] [45] [46] [47] [48]. We verified CNV Radar's ability to accurately infer the copy number status of disease relevant biomarkers using TCGA AML and prostate samples, demonstrating its potential utility in both heme and solid cancers. We further applied CNV Radar to the CASTOR  [32]  and POLLUX  [33]  myeloma clinical trial datasets and found a strong concordance rate with FISH in identification of various myeloma risks associated CNA/CNV events such as amp1q, del13, and del17p. The application of CNV Radar to patient sample repositories, such as TCGA, and to future clinical trials could provide additional prognostic or predictive genomic characterization of patients and help accurately identify patients with high-cytogenetic risk to enable evaluation of therapeutic efficacy in this particularly vulnerable population.
Cancer samples MMRF CoMMpass We used the exome-seq data from the MMRF CoMMpass study  [49]  to assess the accuracy of CNV Radar and to demonstrate its application in the oncology setting. MMRF CoMMpass is an ongoing multi-institution collaboration that will create a comprehensive genomic database of 1000 multiple myeloma patients. Samples are collected at baseline and longitudinally for low-pass WGS  [50] , exome-seq, RNA-seq, and immunophenotyping. FISH is also performed to detect the copy number status for regions associated with myeloma risk (e. g. chromosomes 1q, 13, 17p). The MMRF selected a subset of 109 baseline patient samples and manually curated the FISH results to ensure the accuracy and consistency of data (e. g. done with plasma cell enrichment; FISH results properly transcribed into database).
For the lowpass WGS data, copy number variants were identified by an analysis of differential coverage between each tumor and its matched normal sample. Relative copy number is determined as the log2 difference between the normal and tumor normalized coverage, where normalization is defined as the mean coverage across a 2 kb window divided by the genome-wide coverage. Circular binary segmentation (CBS) algorithm was used to segment copy number data. Allele frequency of common SNPs from matched tumor WES was further used to re-center the copy number data so regions with VAF of 0. 5 became copy neutral. Segments with LFC values less than -0. 25 were defined as deleted, whereas segments with greater than 0. 2 were defined as amplified. The scripts for generating the WGS CNV calls are available at https:// github.
com/tgen/MMRF_CoMMpass/tree/master/tCoNu t_COMMPASS. In this manuscript, we defined the CNV status of patients based on MMRF-reported calls derived from WGS and FISH. To evaluate the performance and accuracy of different CNV callers, we used the exome data from these patients along with 141 normal samples (95 matched and 46 unmatched) to derive their copy number status and compared the results to those derived from the corresponding low-pass whole genome and FISH assays.
TCGA AML TCGA analyzed a collection of genomes of 200 de novo AML patients  [8]. Affymetrix SNP Array 6. 0 was performed on both tumor and matched normal skin samples to derive copy number changes. Briefly, TCGA normalized intensity values using Partek Genomics Suite. Segmentation and copy number calling were done using CBS in the DNACopy package  [51]. From the TCGA AML cohort, we selected 21 patients with known favorable risk and 39 patients with unfavorable risk, analyzed their WES data to make CNV calls with respective methods, and compared the results to the copy number status defined by the SNP6 array as reported in the paper  [8]. With respect to the WES data, we downloaded the raw sequencing files from the Genomic Data Commons (GDC) Data Portal (https://portal. gdc. cancer. gov/), and used respective CNV calling pipelines to detect copy number changes.
TCGA prostate TCGA performed molecular analysis of 333 primary prostate cancers to identify major subtypes among patients as well as potential treatment targets  [36]. Tumor and matched normal specimens were characterized using platforms such as WES and Affymetrix SNP 6. 0 arrays. We selected a subset of 30 patients who showed evidence of recurrent CNAs defined by the SNP6 array data and GISTIC2  [52, 53] , as provided by Firehose (http://gdac. broadinstitute. org), and downloaded the raw  WES data from GDC Data Portal to make CNV calls using respective CNV analysis pipelines. The results were compared to the ground truth defined by SNP6 arrays.
Overview of CNV detection workflow Mapping and pre-processing The CNV Radar analysis pipeline (Fig. 1 ) starts with binary representations of sequence alignment map (BAM) files  [54]  of a tumor and a set of matched or un-matched normal samples that have been aligned, sorted, and indexed using standard alignment tools (e. g. Burrows-Wheeler Aligner (BWA) and SAMtools). Picard MarkDuplicates  [55]  is then used to remove PCR duplicates. The Genome Analysis Toolkit (GATK)  [30]  is used to perform local realignment of reads around indels and to identify single nucleotide variants (SNPs) in the tumor genome. SNP calls are annotated using The Single Nucleotide Polymorphism Database (dbSNP)  [56]  and snpEff  [57, 58]. We utilize all heterozygous common SNPs for CNV detection since if a segment of the genome is amplified or deleted, all of the heterozygous common SNPs in the region will have altered VAF as a result.
Selection of normal references Before CNV calls can be made, a normal baseline must be established. CNV Radar does not require matched tumor-normal samples. Instead, it establishes a normal depth baseline from a normal sample cohort. Due to the properties of the algorithmic implementation (linear regression, see below for more information), CNV Radar automatically up-weights those normal samples with a profile closest to the tumor sample allowing for good performance as long as at least a subset of the normal cohort has a depth profile similar to the tumor sample. However, it may still be beneficial to remove obvious outliers potentially caused by contamination or capture/ sequencing failure. CNV Radar (available at the EA Genomics GitHub page [https://github. com/Expression-Analysis]) provides scripts that perform a clustering analysis of the set of normal samples to identify potential quality control issues.
Calculation of CNV breakpoints Using the filtered VAFs from the GATK calls, we define a VAF score for every detected heterozygous position i in the genome: VAF-score i ¼ VAF i -0:5 j j 3 where VAF i ∈ [0,1]. The exponent of 3 was set after empirical analysis using initial datasets. VAF-scores are then spline-smoothed and fitted values ( d VAF-score j Þ are made for each capture region j. For each chromosome, the d LFC j and d VAF-score j values were multiplied together to form CNV score j and call copy number events as follows: CNV À score j ¼ min max d LFC j ; -3 ; 3 Ã d VAF-score j The ceilings and thresholds for d LFC j ensure that the CNV-score is not completely dominated by the readdepth portion of the formula when analyzing read depth extremes. For single copy loss or gain in a typical admixture with normal cells, d LFC j may range from approximately [-0.
This categorization is illustrated in Fig. 2. The program defaults to threshold values which were established based on those values that provided the greatest accuracy (balancing FP and FN calls) on a training set of data. The truth for this reference data was determined through one or more orthogonal molecular assays and expert consensus manual review of detailed sequencing summaries (variants, variant frequencies, relative read depths) that were independent of any software CN algorithms. These default values should be appropriate in most scenarios, although parameters are available for tuning.
2019) , the intrahemocoelic infection is useful to characterize host-dependent genome-wide transcriptional modulation by MH96 during three different stages of infection using a timeresolved approach. Here, we focus on the interaction between MH96 and G. mellonella, using an in vivo transcriptomic strategy to identify pathogen genes activated under initial insect immunity directly following intrahemocoelic infection, as well as later septicemic and precadaver stages of infection. Differential expression (DE) analysis comparing MH96 transcriptome under in vivo and in vitro conditions identified pathogen genes, including known and putative VFs, that significantly responded either at specific or multiple timepoints throughout the infection process. To further understand the pathobiology of MH96, time-resolved in vivo versus in vitro fold-changes were clustered and functionally annotated using a database of known bacterial VFs.
Bioinformatic analysis Adapter and barcode contamination were filtered using Flexbar (v2. 4) against Illumina adapter and barcode reference sequences (Supplementary Tables  S2  and S3 ). Reads were trimmed in "ANY" mode with minimum overlap of 5, maximum uncalled base of 1, and minimum quality threshold of 20  (Dodt et al. 2012 ). The bbsplit. sh function of the BBMap package was used to quantify and remove contaminating rRNA sequences with minimum ratio of 0. 5, minimum hit number of one, and maximum insertion/deletion length of 500 bp  (Bushnell 2015). All libraries were screened against full-length 5S, 16S, and 23S rRNA sequence from the MH96 genome (see accession information below). To remove host sequences, in vivo libraries were also mapped against the G. mellonella partial 18S and 28S rRNA sequence retrieved from GenBank (Accessions: U65198.
1, U65138. 1, AF286298. 1, AF423921. 1, and X89491. 1) and the G. mellonella immune cDNA library (JG394435-JG406465)  (Vogel et al. 2011). Remaining paired-end reads were aligned to the MH96 genome (Accession: CP010029. 1, GI: 1034308998) using Rockhopper (v2. 03) in verbose mode. Alignment stringencies were increased from default values, such that allowable mismatch threshold was decreased from 15% to 10% of read length and the minimum seed was increased from 33% to 50% of read length. Any residual count data aligning to 5S, 16S, and 23S rRNA genes or any short (<50 bp) non-coding RNA or anti-sense RNA were not considered further in the analysis. Due to low alignment rates to the reference genome, most of the early infection in vivo libraries contained less than the minimal threshold of between 2 and 3 million transcripts per library determined by  Haas et al.
(2012)  as necessary for adequate detection of differentially expressed genes in bacteria. Accordingly, the early infection in vivo libraries were investigated using the iNEXT R package (nboot ¼ 50) (v2. 0. 18)  (Hsieh et al. 2016)  whether the sequence depth adequately captured the majority of MH96 lowabundance transcripts. The RUVseq R package (v1. 16. 0) was used to generate relative log expression (RLE) plots and conduct principle component analysis (PCA)  (Risso et al. 2014). Upper-quartile normalized  (Bullard et al. 2010 ) libraries were filtered for transcripts with greater than five aligned reads in at least two libraries. Normalized expression data were then converted to log2counts-per-million (Log 2 CPM) using the R package limma (v3. 38. 3)  (Smyth 2004; Ritchie et al. 2015)  and the mean-variance relationship was modeled using the "voom" method  (Law et al.
2014). Genome-wide host-dependent responses by MH96 to both in vitro and in vivo conditions were visualized with Circos (v0. 69)  (Krzywinski et al. 2009 ) and putative genomic islands (GIs) were assigned using the webserver IslandViewer 4  (Bertelli et al. 2017). To determine MH96 genes that significantly responded to in vivo conditions, a DE analysis was undertaken by comparing average transcript abundance between in vivo and in vitro libraries sharing equivalent cell densities (i. e. , early infection vs lag growth in broth, middle infection vs exponential growth in broth, and late infection vs stationary growth in broth). Genes with significantly DE were defined by multiple testing with false discovery rate of 0. 05 using the Benjamini and Yekutieli multiple test correction method  (Benjamini and Yekutieli 2001)  using limma.
Time-resolved average in vivo vs in vitro log 2 CPM fold-change values for all genes with significantly DE identified between the in vivo and in vitro treatments (DE analysis described above) were clustered using the fuzzy c-means (FCM) algorithm (Futschik and Carlisle 2005) with the MFuzz R package (v2. 42. 0)  (Kumar and Futschik 2007). A heuristic qualitative assessment was used to identify both the optimal FCM parameter (i. e. , "fuzzifier") and number of clusters  (Kumar and Futschik, 2007). The assessment considered both predicted operon structure (output from Rockhopper) and functional annotations assigned using homology-based search (BLASTx, e-value cutoff ¼ 1 Â 10 À8 ) against the virulence factor database (VFDB) (accessed July 2018)  (Chen et al. 2016). MH96 host-specific factors were identified among clusters containing transcripts with comparably greater median log 2 CPM in vivo fold-change values (! $2) for at least one stage of growth during intrahemocoelic infection.
Next, these host-specific factors were functionally characterized as putative VFs when significant sequence homology was identified against an already characterized VF found in the VFDB. Finally, functional categories of virulence were assigned to all of these putative VFs primarily based on the VFDB-derived annotation, in accordance to our definitions found in Supplementary Table  S4. When VFDBderived annotations were not informative, additional information, such as top-scoring BLASTx hits from the GenBank nonredundant protein database, was used to assign a possible function or otherwise these hypotheticals were left unclassified.