dataesr/scientific-paragraphs-categorization huggingface dataset:
Monoisotopic peaks with S/N Ն 3 were selected and common contaminants were eliminated using the Peak Erazor software (v 2. 0. 1; Lighthouse Data, Odense, Denmark). Annotated spectra for each protein were uploaded at the Peptide Atlas -PASSEL database (dataset identifier PASS00228). Samples for liquid chromatography-tandem MS (LC-MS/MS) were run on a Q-Star Pulsar (Applied Biosystems) instrument fitted with a nanoESI source, after separation by nanoLC in an Ultimate II system (LC Packings, Bannockburn, US). Peptides were separated by reverse phase chromatography (RP Atlantis dC18 NanoEase Column, 75 m ϫ 150 mm, Waters, Milford, MA, USA), using a linear 5-50% acetonitrile gradient into 0. 1% formic acid over 30 min. An electrospray voltage of 2400 V was used. Samples were analyzed in the information-dependent acquisition mode with one full scan followed by three MS/MS scans of the three most intense peaks.
Peak lists were generated from the raw data using the Analyst QS 1. 1 software (Applied Biosystems), with the MASCOT script 1. 6 b21 (Matrix-Science, Boston, MA, USA). MS/MS data were centroided and deisotoped, and only precursor charge states of ϩ2, ϩ3, and ϩ4 were considered. Protein identification from either the MALDI-TOF or the LC-MS/MS results was performed with an in-house MASCOT server v 1. 3 (Matrix Science, Boston, MA, USA), using the human proteins from NCBI (January 2013, 243695 sequences). For database searches, Cys carbamidomethylation and one missed cleavage, plus methionine oxidation were allowed as fixed and variable modifications, respectively. Mass tolerance was set at 50 ppm for MALDI-TOF-derived searches; whereas for LC-MS/MS based searches, tolerance was set at 0. 15 Da for both MS and MS/MS data.
In all cases, a significant MASCOT probability score (p Յ 0. 05) was used as the condition for positive identification. Proteomics analyses were performed at the Proteomics Unit of the Universitat Pompeu Fabra (UPF) and Center of Genomic Regulation (CRG). Protein-Protein Interaction Data-We used BIANA  (18) , a bioinformatics tool for biological data integration, to combine human proteinprotein interaction data from various interaction databases. BIANA is a framework that integrates interaction data from multiple databases into a single database and maps identifiers (i. e. cross-references) in different databases to each other. Using BIANA, we integrated data from the following databases: DIP, MIPS, HPRD, BIND, IntAct, MINT, and BioGRID (all downloaded in May, 2011). The entries in these databases were integrated on the basis of how common the following features were: UniProt Accession, amino acid sequence and Entrez Gene Identifier.
That is, two proteins annotated with the same identifier (one of the three features mentioned above) in two distinct databases were considered the same protein. Prioritization of Bone Metastasis Candidates Using Protein-Protein Interactions-First, a PPIN of the whole human-interactome was built around a set of proteins that are potentially crucial for bone metastatic growth, as identified by proteomics analysis (i. e. root proteins). All the interactions of the root proteins were retrieved from BIANA, provided that the interaction was identified with an experimental method other than a pull-down method. Though suitable for defining protein complexes, interactions from pull-down methods might introduce spurious binary interactions between proteins. Then, gene expression levels were mapped onto the network.
A protein was considered to be differentially expressed if the gene encoding for it was differentially expressed in the microarray experiment. This mapping allowed us to find the active subnetworks: clusters of the network with a significant proportion of proteins produced by up-regulated or down-regulated genes. Next, we calculated a bone metastasis likelihood score for all the nodes in the human interactome using GUILD  (19) , which is network-based disease-gene prioritization software (although only the scores of the PPIN subnetwork were used). GUILD assigns a disease-implication score to each node in the network by disseminating information about roots (potential metastatic growth proteins identified by proteomics analysis) to other nodes through the links in the network. We used 28 out of the 31 root proteins for which we found interactions in the human interactome.
We applied the Net-Combo algorithm implemented in GUILD, which produces a consensus score that incorporates the distance from the nodes to the roots in the network. We first ranked the root proteins with respect to the number of differentially expressed genes in their neighborhood. Next, we ranked the root proteins with respect to the average NetCombo score of their neighbors in the network. We then combined these rankings to produce an ordered list of bone metastatic candidates. The ranks were obtained using the rank function of R software (http:// www. r-project. org/). We applied a Monte Carlo  (22, 23)  permutation test to verify the significance of matching proteins of differentially expressed genes in the neighborhood of root proteins. We calculated an empirical p value by sampling random sets of 28 proteins from the whole human interactome.
We then counted the number of products of differentially expressed genes in the neighborhood of these proteins. We repeated this analysis 1,000,000 times and computed the p value as the ratio of the times we had at least as many proteins of differentially expressed genes in the neighborhood as in the original network. Bone Metastatic Cell Transcriptome Data (BMCT)-The proteinnetwork-based approach to identifying markers of bone metastasis was performed on the results of the previously analyzed microarray hybridization, using the standard Affymetrix protocol (4). The probes were hybridized in duplicate to the Human Genome U133 (HG-U133) set, consisting of two GeneChip® arrays (U133A and B). The final data set consisted of a total of 50 scan files. Each one was obtained using the Affymetrix GeneChip® software, which assigns an intensity that represents a measure of the corresponding transcript abundance to each qualifier in the file.
For flow cytometry, 10 6 cells were seeded in Petri dishes and analyzed at 24 h. For membrane antigen detection, cells were first incubated with the appropriate primary antibody in PBS 1 ϫ and SBF 5%, and then fixed with paraformaldehyde at 4% in PBS 1 ϫ for 15 min at 4 °C. For intracellular antigen detection, cells were first fixed as described and then blocked and permeabilized with fetal bovine serum 20% in PBS 1 ϫ containing 0. 2% Triton X-100% for 1 h at room temperature, beforeincubation with the primary antibody and the appropriate secondary antibody (Table  I ). Flow cytometric analysis was carried out with an FACSCalibur (Becton Dickinson, San Jose, CA) flow cytometer using standard methods. Data were analyzed with CellQuest Pro and FlowJo software. For IF analysis, coverslips were mounted on slides using Vectashield (Vector laboratories, Burlingame, CA) with DAPI, which was used for nucleus visualization.
Preparations were analyzed with an Olympus BX60 microscope (Olympus Optical Co. , Ltd. , Tokyo, Japan) and images were taken and analyzed using a digital camera and Spot 4. 2 software (Diagnostic Instruments, Inc. , Sterling Heights, MI, USA). Confocal analysis was performed when necessary using a Nikon C1-Si confocal laser-scanning imaging system (Nikon Inc, Kawasaki, Japan), built onto a Nikon inverted research microscope Ti-E. Quantification was performed using Image J software. Immunohistochemical Analyses-Cytospin analyses of cell lines were carried out by loading 500 l of a 5 ϫ 10 4 cells/ml suspension and centrifuging the sample at 1000 rpm for 3 min. Slides were dried and fixated in acetone for 5 min before storage. Immunocytochemical techniques were performed with the Novolink Polymer Detection System (Leica Microsystems Inc.
Previous to paraffin inclusion, the bones were treated with HNO 3 5% for 24 h. Specific antibodies (Table  I ) that had been incubated overnight at 4 °C followed by addition of the appropriate secondary biotinylated antibodies were used before hematoxylin (Sigma) counterstaining. Slides were visualized in a Nikon 80i epifluorescence microscope/Nikon Ds-Ri1 digital camera controlled by the Nis-Elements (Br) software.
The coding sequence of ERp57 was analyzed for sites of siRNA targeting using Promega's siRNA Target Designer and the following potential sequence was selected: shERp57 (5Ј-GAGCTTACTGCATGTTTAT-3Ј). This corresponds to nucleotides 2378 -2396 of the ERp57 transcript (GI:67083697). Short hairpin primers were designed around these sequences and annealed before their ligation into the PstI site of the psiSTRIKE Neomycin vector, following the manufacturer's instructions. In a similar fashion, control constructs were produced using scrambled versions of the shERp57 target sequence indicated above (5Ј-GGACCCTTAAATG-GTTTTT-3Ј). Both the ERp57 targeted and the control nucleotide sequences were tested against the Genbank database to avoid undesired interactions with other mRNA transcripts. Plasmids were produced via the Qiagen MaxiPrep protocol (Qiagen, Inc.
The mice were controlled periodically during the experiment until symptoms of metastasis appeared, and metastasis development was monitored by noninvasive bioluminescence imaging. Osteolytic lesions were identified on radiographs as demarcated radiolucent lesions in the bone. The quantification and analysis of the photons recorded in the images was performed using Living Image 4. 0 image analysis software (Caliper, Hopkinton, MA). The number of photons was expressed as photon counts per second (ph/s). In vitro regression models were used to correlate cell number and bioluminescence (supplemental Fig. S4 ) and the final data were expressed as a correlation of the logarithm of cell number versus time after injection. Comparisons were always established one-to-one versus the control transfected cells.
NK cells were sorted from purified peripheral blood mononuclear cells by CD3-(BD Biosciences, San Jose, CA) CD56ϩ (BioLegend, San Diego, CA) selection. For the cytotoxicity assays, after cell sorting in IMDM 10% human serum and 100 U/ml IL-2, NK cells were kept overnight. The next day, the NK and tumor cells were seeded in 96 U-shaped well plates at a ratio of E:T 9:1 and left overnight. IP incorporation of target cells was measured by flow cytometry. Staurosporine was used as a positive control of cell death at 1 M. Statistical Analysis-The statistical analysis of in vitro experiments was performed using SPSS for Windows. Two-way analysis of variance was used to compare the mean expression levels. In all the analyses, differences were considered significant when p Ͻ 0. 05. Microsoft Excel was used to plot the graphs.
46-fold), Stathmin 1 (1. 25-fold), Cathepsin D (0. 50-fold), and Galectin 1 (0. 005) in BO2 cells compared with 231 cells. In addition, immunohistochemical analysis showed expression of these proteins in experimental bone metastasis from BO2-injected mice (supplemental Fig. S1D ). We examined previous reports for further support on the relationship of these proteins with breast cancer and/or metastasis (supplemental Table S5 and supplementary references for supplemental Table  S5 ). The Protein-Protein Interaction Network Reveals that Downregulated HLA Class I Molecules Interact with ERp57 in Bone Metastatic Cells-The integration package of BIANA was used to build a PPIN around the 31 proteins identified above (i. e. root proteins; proteins known to be involved in bone metastatic breast cancer).
We performed Western blot and immunofluorescence microscopy analysis of bone metastatic cells using specific antibodies to experimentally validate the relationship between HLA class I underexpression and ERp57 protein overexpres-FIG. 1. Flow chart of the work with the in silico bone metastasis analysis using transcriptomic and proteomic data to find functions that are differentially expressed between 231 and BO2 cells. A, A protein-protein interaction network (PPIN) was built from a target set of proteins identified by mass spectrometry (n ϭ 31). The PPIN connects 28 root proteins (yellow), which interact with 1328 nodes (gray) or interacting proteins. B, The previously performed genomic and proteomic analysis was performed on 93 matching proteins included in the PPIN. Gene expression levels are indicated in green (underexpression) or red (overexpression).
Acknowledgments-We thank  Ms. Berta Martin  for her expert proteomics technical assistance and  Dr Cristina Chiva  from the  Proteomics Unit of the Universitat Pompeu Fabra (UPF)  and the  Center of Genomic Regulation (CRG). Proteomics analysis were performed in the  CRG/UPF Proteomics Unit , part of the  Spanish Proteomics Network ProteoRed, Instituto de Salud Carlos III. We acknowledge all the partners of the  METABRE consortium  for their collaboration and stimulating criticism.
Moreover, access to physical maps and sequences helped to refine collinearityrelationships between wheat and the other grass genomesby providing a higher level of resolution than genetic or cytogenetic mapping  [15, 16, 18]. The strategy used to build the physical map of wheat chromosome 3B was based on a high-information-content fingerprinting method  [19]  and FingerPrinted Contigs (FPC) software  [20, 21]  for the assemblies. It resulted in 1,036 contigs with an N50 of 778 kb covering 82% of the chromosome  [14]. To improve physical assembly in complex genomes, new software, called Linear Topological Contig (LTC),has been developed recently as an attractive alternative to FPC. It enables longer, better ordered and more robust contigs to be built compared to FPC contigs  [22]. Physical maps are only useful when they are anchored to genetic maps and traits with markers.
Results FingerPrinted Contigs and Linear Topological Contig assemblies of the 1BL physical map A 1BL-specific BAC library, containing 92,160 clones originating from sorted wheat chromosome 1BL of Chinese Spring and representing 15. 4x coverage of the arm  [12] , was fingerprinted with the SNaPshot technology. A total of65,413 high quality fingerprints (71%) wasobtained and used to build a physical map. A first automated assembly was performed with the FPC software  [20, 21] following the guidelines adopted by the IWGSC  [26]. This resulted in an assembly of 43,523 fingerprints into 3,030 contigs representing 807 Mb(151% of chromosome 1BL) with a N50 of 434 kb and a L50 of 391. A minimal tiling path (MTP) of 8,597 clones was designed and re-arrayed for further marker screening and analyses.
Sixty-three -dimensional (plate, row and column) pools from the MTP and 240 plate pools from the whole 1BL BAC library were produced. During the course of the project, a new software -LTC  [22]  -specifically developed to build physical maps in complex genomessuch as wheat,became available. To improve the assembly of the 1BL physical map for future sequencing, we performed an automated LTC assembly using the same 65,413 high quality fingerprints. It resulted in an assembly of 41,940 fingerprints (including 94. 4% in common with the FPC assembly) into 694 contigs representing 502 Mb (94% of the chromosome arm) with a N50 value of 961kb and a L50 of 162. The maximum contig size was of 5,800 kb in the LTC map, three times longer than the1,780 kb in the FPC. This improved LTCmap was used as a template for adding the marker and order information and for building a final version of the map.
A combination of high throughput approachesenables the construction of a 1BL physical map anchored with more 5,000 molecular markers At the beginning of the project, there were only 171 1BLspecific PCR markers (114 single sequence repeats (SSR) and 57 restriction fragment length polymorphisms (RFLP)) available publicly in the GrainGenes database  [27]. Thus, to develop a high density integrated physical map of chromosome 1BL, that is, a map comprising BAC contigs anchored to genetic and cytogenetic maps with a high number (>1,000) of molecular markers, we developed new molecular markers and anchored them to the 1BL physical contigs and genetic or cytogenetic maps. The new 1BL markers were obtained from three different sources. The first source was2. 17 million sequence reads obtained by the Roche 454 technology on amplified DNA from sorted chromosome 1BL of Chinese Spring  [28] , used to develop insertion site-based polymorphism (ISBP)  [29] and SSR markers.
This represents a density of onemarker per 0. 26 cM. To establish links between the physical, geneticand cytogenetic maps, we first screened the 63 three-dimensional pools of the MTP and the 240 plate pools of the complete 1BL BAC library with 465 1BL-specific PCR. Of these, 431 markers amplified at least one MTP row, column and MTP plate pool and/or several plate pools. Deconvolution of the information was performed using a homemade Perl script (named DSMP. pl) for the MTP pools results and the Elephant (electronic physical map anchoring tool)  [33]  software for the plate pools results (see Materials and methods). This led to the assignation of 416PCR markers (320 ISBPs, 70 SSRs, 22 COSs and 4 RFLPs) to individual BACs (Additional file 1). The information was integrated into the final version of the physical map obtained by LTC, resulting in the anchoring of 241 contigs with the 416 markers.
A zipper of the wheat chromosome 1BL was performed by integrating information from a wheat 1BL genetic map comprising 242 gene-based single nucleotide polymorphism markers obtained by genotyping by sequencingof the International Triticeae Mapping Initiative (ITMI) population  [38] , 198,968 sequence contigs from the Illumina sequencing of sorted wheat chromosome 1BL performed by the IWGSC [10], wheat ESTs from the HarvEST database  [39] , barley full-length cDNA, and the rice, sorghum and/or B. distachyon genes (Additional file 3). The 1BL zipper provided a virtual order for1,593wheat loci including 1,433 genes organized in eightsyntenic blocks containing between 29 and 506 genes (average = 179) and 2. 6 markers per block (min = 1, max = 8) (Figure  3D ;Additional file 3). In total, 429 genes with an average of 51 genes (8 to 132) per syntenic block were shared between the physical map-based and zipper-based virtual gene orders.
Decreasing the initial stringency of the FPC assembly to compensate for lower fingerprint quality is not an optionin wheat because the high percentage of repetitive sequences leads to a large numberof chimerical contigs  [14]. Dealing with such problems and improving assembly in wheat was one of the rationales behind the development of theLTC software  [22]. Here, we demonstrated with a real case study that LTC improves assembly even with a suboptimal quality of fingerprints. Assembly with LTC resulted in a robust 1BL physical map covering 94% of the chromosome arm and with an N50 of 1,128 kb, that is,about threetimes longer than with FPC andgenerally higher than the values achieved so far with FPC in wheat and barley  [14, 44]. Whole Genome Profiling (WGP™) that relies on sequence-based fingerprinting of BACs to buildphysical mapshas been developed recently  [45]  and was evaluated for wheat using a subset of the wheat 3B chromosome BAC library  [46].
BAC Fingerprinting and data processing A chromosome 1BL-specific BAC library named TaaCsp1BLhAcontaining 92,160 clones originating from sorted 1BL wheat chromosome of Chinese Spring was constructed as described by Simkova et al. [91]. Fingerprinting of all 1BL BAC clones was performed as described in Paux et al. [14]. Briefly, it consisted in the digestion of BAC DNA by five restriction enzymes (BamHI, EcoRI, XbaI, XhoI and HaeIII); labeling of the DNA fragments with the SNaPshot™ Multiplex Labeling Kit solution (Applied Biosystems, Foster City, CA, USA); and estimation offragment sizes on an ABI 3730XL DNA capillary sequencer (Applied Biosystems). Data were processed using the GeneMapper, Finger-Print Background removal (FPB) and GenoProfiler programs to size the fragments and remove background noise and contaminations.
In particular, raw electropherograms produced by the ABI Data Collection software were analyzed using GeneMapper: fragment sizing was performed without the 250base pair (bp) band of the 500LIZ GeneScan size standard file. Peak areas, peak heights and fragment sizes of each BAC fingerprint profile were exported in text format. Spurious peaks (background noise, vector bands, partial or unspecific digestions) and bands out of the range of 50 to 500 bp were removed by FPB; this software was also used to discard substandard profiles that may negatively affect contig assembly and to convert data into a format compatible with the GenoProfiler and FPC programs. Geno-Profiler was used to detect cross-contaminated clones in 384-well and 96-well plates and to remove negative controls. At the end, a total of 65,413 highquality fingerprints (71%) were obtained with an average number of scored bands per BAC fingerprint of 107 ±25 (ranging from 30 to 216).
BAC assembly Two software packages were used to build the physical map using the 65,413 high quality fingerprints: FPC and LTC. With the FPC software, automated assemblies were performed using the methodology described by Paux et al. [14]  for the construction of the physical map of chromosome 3B. Briefly, the initial build of chromosome 1BL was performed by incremental contig building with a cut-off of 1e -75. These were subsequently run through single-to-end merging (Match: 1) at six successively higher cut-offs ending at 1e -45. The DQer function was used at each cut-off to break up all contigs that contained more than 10% of Questionable (Q) clones (Step: 3). The following parameters were used to establish the FPC physical map: a tolerance of 12, a gel length of 56,000 and a From End value of 55.
With the LTC software, the automated assemblies were performed using the following methodology: the same metric used with FPC, called the Sulston score, was used to calculate clone overlaps. Numerous groups of overlapping clones, called net of significant overlaps, were obtained with a cut-off of 1e -15. Then, subnetswere obtained at a cut-off of 1e -25 and used to build contigs. All contigs with 5 to 999 clones and with linear topology (net width ≤1) were validated. All contigs with a width ≥2 were checked manually for their linearity. If only one clone explained the non-linearity, the contigs were validated because this non-linearity was likely due to the bad quality of the fingerprint for this clone. Then, all contigs with more than 999 clones and all non-linear contigs were broken up by the elimination of Q clones and Q overlaps.
The same featuresas described previously (linearity and number of clones) were used to validate contigs. The remaining contigs were broken up by increasing the stringency (cut-off of 1e -30 ) and then elimination of Q clones and Q overlaps for the non-validated contigs. The following parameters were used to establish the LTC physical map: a tolerance of 12, a gel length of 60,000,and a minimum contig size of five clones. A manual version of the LTC physical map constructionwas then performed by identifying fingerprint overlaps with a lower stringency (cut-off of 1e -15 ), supported by information provided by contig anchoring in deletion bins with molecular markers as described in Paux et al. [14]. A home-made Perl script called FingMergingContigs. pl was developed to automate the identification of the contigs that need to be manually merged.
Minimal tilling path design and 3-dimentional pooling The MTP was selected from the FPC automated physical map using the FPC software. The following parameters were used: a minimum FPC overlap of 30, a maximum FPC overlap of 250, a From End of 0 and minimum shared bands of 12. The three-dimensional pooling of the 8,597 clones of the MTP selected from the 1BL BAC library was performed as described in Paux et al. [14]. Itresulted in 24 column pools, 16 raw-pools, 23 plate pools, and a super pool containing all 8,597 BACs clones.