dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we found that in the melanoma sample 0D5P, most of the noncHLAIp derived from the Ribo-Seq inferred database originated from source genes harboring upstream ORFs (uORFs). Notably, uORFs can trigger the nonsense-mediated decay of messenger RNAs (mRNAs) and provide a rich source of noncHLAIp  [55] [56] [57]. While a previous study showed that the presentation of noncanonical peptides was enhanced by inflammatory stimuli, the presentation of only specific HLA peptides was documented  58. In contrast, our large-scale analysis of both DAC-and IFNγ-treated cells did not detect profound changes in noncHLAIp presentation, although non-coding source genes were induced. Hence, we hypothesize that low copy numbers of such noncHLAIp remain a limiting factor for their presentation. Moreover, corroborating prior research  27  , we report the enrichment of noncHLAIp originating from the C-terminus of source protein sequences.
Translation products of such presumed non-coding regions could be considered defective ribosomal products that are expected to be unstable and rapidly degraded, likely bypassing the proteasome  59. ZMAT3 ZFYVE16 ZFAND5 WSB1 UGCG UBAP1L TTYH3 TRIB2 STXBP1 ST3GAL6 SLCO5A1 SLC38A8 SERPINE2 SEMA6A SELEN OW SCD SAT1 S100A6 RPL9 RPL7A RPL6 RPL41 RPL39 RPL37 RPL36 RPL35A RPL35 RPL34 RPL32 RPL29 RPL27 RPL26 RPL24 RPL15 RPL12 RPL11 RAB17 PSAP PRUNE2 PLXNC1 PHPT1 NPDC1 NORAD NEAT1 MYO5A MITF MGMT MALAT1 LINC00520 LAMB2 HIPK2 GPR161 FTL FBXW5 FAM210B EIF4B EEF2 DAB2 CTSL CTNNB1 CHPF CHMP1B CERK C9orf16 C11orf96 BBC3 ATP6V1G1 AL162457. 2 ABHD2 ABCB5 Given the lack of complete tissue immunopeptidomics reference libraries from healthy donors, we propose a workflow to retrospectively search for tumor-specific non-coding source genes with publicly available RNA-Seq databases (such as GTEx  46  ).
The peptides that were identified by MS across the investigated samples, and therefore shared, are outlined in the rightmost heatmap. Validation by PRM was performed for multiple noncHLAIp across the corresponding samples and are denoted with cross markings. b NoncHLAIp identified across a large collection of immunopeptidomics datasets (ipMSDB) consisting of both cancer and healthy samples. Tumor-specific noncHLAIp were re-identified and a trend of enrichment in cancer samples was observed. The noncHLAIp sequences can be found in the source data file. Cancer samples are labeled in shades of blue, and the star symbol include tumor metastases, myeloma, uterine, brain, and liver cancer. Healthy samples are indicated in shades of red, and the hashtag symbol include fibroblast cells and epithelial cells.
Sequencing was performed on the Illumina® MiniSeq™ System (Illumina) using a paired end 2 × 150 bp protocol. The data were analyzed with Assign TruSight HLA v2. 1 software (CareDx). Immunoaffinity purification of HLA peptides. We performed HLA immunoaffinity purification according to our previously established protocols  39, 72. W6/ 32 and HB145 monoclonal antibodies were purified from the supernatants of HB95 (ATCC® HB-95™) and HB145 cells (ATCC® HB-145™) using protein-A sepharose 4B (Pro-A) beads (Invitrogen), and antibodies were then cross-linked to Pro-A beads. Cells were lysed with PBS containing 0. 25% sodium deoxycholate (Sigma Aldrich), 0. 2 mM iodoacetamide (Sigma Aldrich), 1 mM EDTA, a 1:200 protease inhibitors cocktail (Sigma Aldrich), 1 mM phenylmethylsulfonylfluoride (Roche), and 1% octyl-beta-D glucopyranoside (Sigma Alrich) at 4 °C for 1 h.
The GEN-CODE comprehensive gene annotation version 221. 2 was downloaded from the GENCODE website (https://www. gencodegenes. org/human/release_22. html) and used to define the protein-coding and non-coding gene features, including chromosome position, transcript structure, and transcript and protein sequences. Here, the human reference genome GRCh38/hg38 was downloaded from the NCBI (https://www. ncbi. nlm. nih. gov/assembly/5800238) and used as the genome assembly. The RNA-Seq reads were aligned to the GRCh38/hg38 reference genome using RNA-Star (v2. 4. 2a; https://github. com/alexdobin/STAR). Gene expression was normalized and calculated as FPKM values by Cufflinks (v2. 2. 1) (https:// github. com/cole-trapnell-lab/cufflinks). The gene-level RNA expression data for both protein-coding and non-coding genes were used for downstream gene expression analysis  32, 74.
RNA-Seq data processing for TE expression analysis. We developed an analytical pipeline that was capable of assigning TE-derived RNA-Seq reads to single loci in >95% of the cases. Reads from the investigated samples and public data from GTEx were mapped to the human (GRCh37) genome using hisat2 v. 2. 1. 0  75. Counts on genes and TEs were generated using featureCounts 1. 6. 2  76. To avoid read assignment ambiguity between genes and TEs, a gtf file containing both was provided to featureCounts. For repetitive sequences, an in-house curated version of the Repbase database was used (fragmented LTR and internal segments belonging to a single integrant were merged). Only uniquely mapped reads were used for counting genes and TEs. Finally, features that did not have at least one sample with 20 reads were discarded from the analysis.
Normalization for sequencing depth was performed for both genes and TEs using the Trimmed Mean of M values (TMM) method as implemented in the limma v. 3. 36. 5 package of Bioconductor 77 and using the counts on genes as the library size. Personalized sequence databases from non-coding transcripts. The curated set of human ENCODE non-coding transcripts (GRCh37 reference assembly) was downloaded from https://www. gencodegenes. org/human/release_24lift37. html. ORFs in all three forward reading frames were identified using a stop-to-stop strategy. The minimum peptide length was set to eight amino acids, and the longest polypeptide identified was 3644 amino acids. Unless otherwise mentioned, to build the personalized protein fasta file, we selected transcripts from non-coding genes that were expressed in each sample (i.
e. , FPKM > 0) and translated them in all three forward reading frames. Personalized databases with variants. GENCODE v24 (GRCh37 human reference assembly) downloaded from https://www. gencodegenes. org/human/ release_24lift37. html was chosen as the standard reference dataset. Whole-exome sequence reads were aligned to the GRCh37 human assembly with BWA-MEM  78  , and variants were predicted using GATK framework v3. 7 and Picard Tools v2. 9. 0  79. Small nucleotide polymorphisms (SNPs) were defined as variants present in both tumor and germline samples, and somatic mutations (somatic nucleotide variants (SNVs) and indels) were defined as being present in only tumors. The GENCODE comprehensive gene annotation file, in GFF3 format, was parsed to extract genomic coordinate information for every exon in each protein-coding transcript, and those coordinates were compared with sample-specific variant coordinates to derive non-synonymous amino acid changes within each protein.
For every sample, we created a separate fasta file for which residue mutation information was added to the header of the affected translated protein-coding transcripts, in a format compatible with MaxQuant v1. 5. 9. 4i  80. Mass spectrometry database search. We used two widely used search tools: Comet 2017. 01 rev. 2  34  and the Andromeda search engine within MaxQuant v1. 5. 9. 4i  81. Both Andromeda and Comet allow searching for peptides with and without variants. Andromeda matched the MS/MS spectra of each sample against the personalized reference libraries (mentioned above). Similarly, the variants were annotated in the PEFF format (http://www. psidev. info/peff) for Comet. Both search tools were run with the same principal search parameters: precursor mass tolerance 20 ppm, MS/MS fragment tolerance of 0.
02 Da, peptide length of 8-15 when searching only HLA-I peptides and 8-25 for both HLA-I and HLA-II peptides and no fixed modifications. For samples 0D5P, 0NVC and 0MM745, oxidation (M) and phosphorylation (STY) were set as variable modifications; for the remaining samples only oxidation (M) was included as a variable modification. A PSM FDR of 3% was used for Andromeda as a first filter, and non-canonical reference sequences were loaded into the "proteogenomics fasta files" module for FDR calculations for proteome-derived and non-canonical sequences. For each spectrum the annotated PSMs with the highest score were kept (including the decoy hits calculated by Andromeda from reversed protein sequences) and stored in binary files. To assure that non-canonical peptide sequences did not match other proteincoding genes, all peptides found by Andromeda or Comet were aligned against an up-to-date UniProt/TrEMBL sequence database (95,106 protein sequences of the human reference proteome up000005640, with isoforms, downloaded 26/09/2018) using an algorithm built in NewAnce.
Leucine and iso-leucines were treated as equal since they are not distinguishable by MS. If peptides were found to match standard UniProt sequences, they were assigned as proteome-derived with the UniProt IDs. However, we retained non-canonical TE peptide sequences that matched annotated TEs that were integrated into the human reference in UniProt. Comet PSMs were read from Comet pep. xml files and all peptides were aligned against the UniProt database as described above. Equivalent to the Andromeda PSMs processing, PSM were annotated and the highest scoring PSMs were stored in binary files. Comet PSM processing was implemented in Java and utilizes the MzJava class library  82. As described in detail below, it consisted of two main steps: first, three Comet scores XCorr, deltaCn, and spScore and the spectrum charge were combined, and second, the FDR was calculated separately for proteome-derived and non-canonical peptides.
The first step boosted the overall number of identified PSMs at a given global FDR, whereas the second step limited the number of false positives in the group of non-canonical peptides at a given global FDR. All PSMs resulting from the Comet binary files were split into three sublists with PSMs of charge (Z) 1 (applicable to HLAIp only), 2, and charge 3 or higher. Further, the three Comet scores XCorr, deltaCn and spScore were considered (the expect score was left out because it depends on the size of the sequence database). In order to calculate the FDR for three-dimensional (3D) vectors x ¼ XCorr; deltaCn; spScore ð Þ , the 3D spaces (one 3D space per charge state Z) were partitioned into small cells with 40 intervals in each dimension (Supplementary Fig. 1a ). The PSMs in the sublist of charge Z were then parsed and for each cell, the number of wrong hits (n 0 ) was set to the number of decoy PSMs in that cell, and the number of true hits (n 1 ) was set to the number of target (nondecoy) PSMs minus n 0.
The 3D probability distributions were estimated by dividing the counts in each cell by the total counts summed over all cells resulting in a distribution for each charge state Z for true p xjZ; H ¼ 1 ð Þ ð Þ and wrong p xjZ; H ¼ 0 ð Þ ð Þ PSMs. In order to obtain smoother distributions, both true (n 1 ) and decoy (n 0 ) counts were averaged over a 9-cell nearest neighborhood. This 3D histogram based approach has the advantage that it does not require strong assumptions about the shape of the probability distributions, and in contrast to one-dimensional (1D) projection methods, it does take into account the full 3D structure of the score space. On the other hand, it requires fairly large datasets with >100,000 PSMs. The local FDR (lFDR) is the probability that a PSM within a given cell is wrong, whereas the global FDR is the probability that a PSM in the final result list from all cells is wrong.
At the same FDR of 3%, the 3D histogram approach was able to boost the number of unique peptides for both proteome-derived and non-canonical peptides by 22% and 13%, respectively. Importantly, the percentage of predicted HLA binders and the standard error in hydrophobicity index calculation by SSRCalc remained unchanged (Supplementary Fig. 1c, d ), indicating that the 3D method used in NewAnce did not inflate the error. However, Supplementary Fig. 1c  also reveals that the percentage of predicted binders is low for the group of non-canonical peptides (only 55% compared to 95% for proteome-derived peptides), indicating a large portion of wrong PSMs in the non-canonical group. This phenomenon has been reported before  28  and is due to a misbalance of true and false hits in the noncanonical sequence databases.
Non-canonical sequence databases are typically very large, and they contain mostly sequences that have low probability to contribute to true hits. This causes a strong prevalence for wrong PSMs or a low π 1 =π 0 ratio (π 1 =π 0 ratio is the total number of true PSMs divided by the total number of wrong PSMs) compared to the proteome-derived database. In order to tackle this problem, we implemented an approach that estimates the lFDR values separately for non-canonical and proteome-derived PSM groups. Since there are only several hundreds of non-canonical PSMs, we could not use the 3D histogram approach directly for the non-canonical PSM group. Instead, we assumed that the probability distributions p xjZ; H ¼ 0; 1 ð Þ are the same for noncanonical and proteome-derived PSMs, and that the π 1 =π 0 ratios strongly depend on the PSM group.
1e-g ). In order to assign peptides into source protein groups, we implemented a greedy bipartite graph protein grouping algorithm  84. The total and unique peptide counts were calculated for each protein. To calculate the adjusted peptide counts we sorted the proteins in each group by decreasing number of peptides and for each protein removed the peptides of all proteins higher up in the list. In order to test the robustness of our approach, the 2597 PSMs of identified noncHLAp were re-searched against the human reference proteome UniProt database concatenated with the list of non-canonical peptide sequences, including six common modifications. The variable modifications included were 15. 9949 Da for oxidation on M, 42. 010565 Da for acetylation on the N-terminus, 79. 966331 Da for phosphorylation on STY, 119.
004099 Da for cysteinylation, 0. 98402 Da for deamidation NQ and 57. 021464 Da for carbamidomethyl on C. Comet was employed (same parameters as above, but no FDR) to investigate whether PSMs would better fit another possible proteome-derived (modified) sequence based on XCorr. The results are reported in Supplementary Data 4 and 5. To build the ipMSDB database, we searched 1102 immunopeptidomic raw files with Comet (PSM FDR of 1%, as described above), and the Apache Spark cluster computing framework  85  was used to process the results and calculate the FDR. The samples were annotated with basic biological information for further statistical analysis. Ribo-Seq: experimental protocol. Ribo-Seq was performed according to Calviello et al. 86. Ribo-Seq libraries were derived from adherent melanoma 0D5P cells that were 80% confluent in 10 cm tissue culture dishes.
Reads with up to 20 multi-mapping positions were included, with multi-mapping reads beings separately treated in subsequent periodicity analysis. The RIboseQC pipeline v1. 0 89 was used to deduce P-site positions from the Ribo-Seq reads, and the P-site data were then used as input into the ORFquant pipeline v0. 9  90  in combination with custom R scripts  86  for ORF calling. The ORFquant pipeline searches for the periodic ribosomal footprint pattern characteristic of translated ORFs using a supplied database of transcripts, yielding a set of ORFs corresponding to known coding regions, as well as ORFs originating from UTRs, non-coding RNAs, intron retentions, and read-through events. The 0D5P samples had a median of 2. 8 million reads mapped to coding sequences per sample, which constituted a median of 81% of the total reads (Supplementary Table  2 ).
Since the false-positive rate of periodicity based ORF calling is thought to be tolerant of non-periodic sources of noise such as genomic contamination, we included all samples for 0D5P. ORFs were called in both individual libraries and in the pooled set of all libraries for 0D5P, and ORFs that were fully contained within ORFs detected in another library were merged. ORFs were tested for periodicity, by a multitaper test  86  , and those with a p-value below 0. 05 were retained for analyses. Polypeptide sequences in fasta format were generated from the coordinates of these ORFs and used for both validation of the peptides found using the RNA-Seqbased database and as a de novo-assembled database for the subsequent round of peptide detection. Peptides were considered validated by Ribo-Seq if they matched anywhere within the translated ORF sequences.
Ribo-Seq profile plots were plotted with P-site numbers per-base on a log2 (n + 1) scale. The 10x genomics pipeline and gene expression analyses. For single-cell library preparation on the 10x Genomics platform, the Chromium Single-Cell 3′ Library and SingleCell 3ʹ Reagent v3 were utilized, together with the 10x Chromium single-cell controller instrument in accordance with the official CG000183 RevA user guide. A total of 1692 0D5P cells were captured for single-cell transcriptomics. The resulting cDNA libraries were sequenced on NextSeq v 2. 5 (with Illumina protocol #15048776). Cell Ranger v. 3. 0. 1 software (10x Genomics, https://support. 10xgenomics. com/singlecell-gene-expression) was used to process data generated using the 10x Chromium platform, with a restriction of including only 1400 cells to avoid cells or debris with low unique molecular identifier (UMI) counts.
5. 5. 3, RStudio 3. 5. 1 and Python 3. 6. Specifically, the boxplots in Fig. 8c , Fig. 9a , b, Supplementary Fig. 1c, d  and 1f -h, Supplementary Fig. 3a, b  and Supplementary Fig. S4a-l  were generated using the standard settings in either RStudio or GraphPad Prism. The boxplot settings were: Hinges (25% and 75%), with the median plotted. For Fig. 8c  and Supplementary Fig. 1c, d  and 1f -g, the notch is additionally shown at +/-1. 58 IQR/sqrt(n), where IQR is the interquartile range (difference between 75and 25-percentile) and n the number of data points. A median at the notch edge corresponds to a 95% significant difference (p-value = 0. 05). Sample sizes and pvalues for Supplementary Fig. 1c, d  and 1f -g can be found in the Source Data File. In Fig. 9a-b , Supplementary Fig. 1h , Supplementary Fig.
For noncHLAp, the gene with the highest transcript expression was allocated for further analyses if the peptide map back to more than one non-coding source gene, unless otherwise indicated. Assessment of HLAIp sampling. For HLAIp sampling analyses, peptides were assigned to source protein groups as described above. Adjusted peptide counts were taken, summed over a gene, and subsequently matched to their corresponding expression values (either transcriptome or translatome based). Normalized sampling corresponds to the adjusted peptide count per protein, normalized by the protein length. The correlation between gene expression or the spectral coefficients of 3-periodic signals in Ribo-Seq data and HLA presentation were assessed by fitting a polynomial curve of degree 3 to each dataset. Pearson correlation was used to assess the correlation between the fitted curve and the data.
Peptide position analysis. For peptide position analysis within a protein sequence (Supplementary Fig. 3 ), proteome-derived datasets fitting to the length distribution of the 95% confidence level of the lncRNA dataset were selected. Then, the position of the HLAp, relative to the full protein sequence, was calculated for source lncRNA and proteome-derived sequences. Since the data were not normally distributed, the Wilcoxon test was utilized for statistical analysis. PRM analyses. For analyses of PRM statistics, MS-based intensities were taken from the initial MaxQuant peptide table output. TAAs for PRM and further comparative analyses were selected from a non-exhaustive list of known and clinically relevant TAAs. GTEx RNA expression analyses. Tissue-specific gene expression data was downloaded from GTEx, a public resource that contains data from 53 non-diseased tissues across nearly 1000 individuals  46.
We used a custom R script to retrieve gene expression values, based on publicly available GTEx v7 data. In the case of multiple transcripts matching the same entry, expression data for the most expressed transcript were used. The 90th percentile expression of the gene in the tissue-was reported. The FPKM expression units of the investigated sample were converted into TPM units for comparison with the GTEx data. The R package "Complex-Heatmap v1. 99. 4" 93 from the Bioconductor suite was used to draw heatmaps. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank the patients for their participation in this study and the  Center of Experimental Therapy CHUV, Lausanne , for the collection, generation and allowed use of samples. Library preparation and sequencing were performed at the  Lausanne Genomic Technologies Facility, University of Lausanne, Switzerland  (https://www. unil. ch/gtf/en/home. html). We thank the  Gene Expression Core Facility at the École polytechnique fédérale of Lausanne  for the scRNA-Seq data. We would also like to express our thanks to  Julien Schmidt  and  Philippe Guillaume at the Protein and Peptide Facility and Tetramer Core Facility ,  University of Lausanne  for the synthetic peptides. Further, we thank  Raphael Genolet  and  Lise Queiroz  for the HLA typing of the samples. We thank  Fabio Marino  for providing a few datasets included in ipMSDB.
Data availability Sequence data have been deposited into the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession numbers EGAS00001003723 and EGAS00001003724. MS raw files, corresponding fasta reference files and NewAnce outputs have been deposited into the ProteomeXchange Consortium via the PRIDE  94  partner repository with the dataset identifier PXD013649. The GENCODE v221. 2 can be accessed through https://www. gencodegenes. org/human/release_22. html The human reference genome GRCh38/hg38 can be accessed through https://www. ncbi. nlm. nih. gov/ assembly/5800238. Human ENCODE non-coding transcripts can be accessed through https://www. gencodegenes. org/human/release_24lift37. html. GTEx v7 can be accessed through https://www. gtexportal. org/home/datasets.
The UniProt/TrEMBL database can be accessed through https://www. uniprot. org/proteomes/UP000005640. The source data underlying Figs. 1 2 3 4 5 6 7 8 9 and Supplementary Figs. 1 2 3 4 5 6 7 , where applicable, are provided as a Source Data file. All other data are available from the corresponding author on reasonable request.
The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that shortread aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden.