dataesr/scientific-paragraphs-categorization huggingface dataset:
Targeted sequencing is commonly used in clinical application of NGS technology since it enables generation of sufficient sequencing depth in the targeted genes of interest and thus ensures the best possible downstream analysis. This notwithstanding, the accurate discovery and annotation of disease causing mutations remains a challenging problem even in such favorable context. The difficulty is particularly salient in the case of third generation sequencing technology, such as PacBio. We present MICADo, a de Bruijn graph based method, implemented in python, that makes possible to distinguish between patient specific mutations and other alterations for targeted sequencing of a cohort of patients. MICADo analyses NGS reads for each sample within the context of the data of the whole cohort in order to capture the differences between specificities of the sample with respect to the cohort.
MICADo is particularly suitable for sequencing data from highly heterogeneous samples, especially when it involves high rates of non-uniform sequencing errors. It was validated on PacBio sequencing datasets from several cohorts of patients. The comparison with two widely used available tools, namely VarScan and GATK, shows that MICADo is more accurate, especially when true mutations have frequencies close to backgound noise. The source code is available at http://github. com/cbib/MICADo.
Therefore, only few variant callers are recommended for PacBio data, such as the aforementioned GATK pipeline and the PacBio's Quiver algorithm. In the clinical setting, distinguishing between true mutations and artifacts introduced by sequencing errors and variant calling tools, is essential. Indeed, systematic molecular characterization of relevant genes within patient cohorts is key for understanding cancer initiation and progression. When sequencing data obtained from a patients' cohort is analyzed for the presence of SNVs, identification of genetic variants is performed for each patient's sample individually. These individual results can be further jointly analyzed in the downstream statistical analysis. Such standard analysis workflows do not fully exploit the whole tumor cohort raw data.
Concomitantly, multiple studies have shown the existence of specific error profiles in second generation sequencing. For example, GC rich regions and the extremities of sequence reads exhibit higher error rates  (Allhoff et al. , 2013) , and both Roche 454 and Ion Torrent encounter difficulties in the homopolymer regions  (Ross et al. , 2013). Based on these observations,  Bansal (2010)  has proposed a statistical method that identifies rare and common variants in DNA pools of diploid individuals. However, the diploidy requirement prevents its application to cancer data where samples are highly heterogenous. To overcome such difficulties in PacBio clinical applications, some studies develop ad-hoc solutions to perform their analyses. For example, in order to take advantage of the long read technology for the analysis of highly polymorphic regions such as e.
g. , human leukocyte antigen (HLA) genes, the authors must resort to extremely stringent filtering of sequencing reads thus avoiding noisy data, but also potentially getting rid of useful information  (Shukla et al. , 2015). When classical variant calling is used, such as GATK, the authors still make use of additional ad-hoc strategies. In  Orkunoglu-Suer et al. (2015)  the authors completely exclude indels known to be a particular difficulty of PacBio sequencing, while  Smith et al. (2012)  restrain the analysis to certain loci. There is thus a lack of a generic and efficient algorithmic solutions for this application niche. In this manuscript we pursue the idea that analyzing sequencing data produced for a cohort of patients as a whole should make it possible to distinguish between real patient mutations and other alterations, including sequencing errors.
Indeed, one sample can be analyzed in the context of the whole dataset, serving itself as statistical basis for filtering out systematic alterations. This idea is even more pertinent in the case of sequencing data carrying non-uniform sequencing errors as, shown in this paper, it is the case for PCR targeted PacBio data. We introduce a method called MICADo that distinguishes patient-specific from cohort-specific alterations and show that it efficiently performs even on samples carrying SNVs in the context of contamination by germline DNA and in the presence of technologic artifacts. MICADo is based on the well-known representation of NGS sequencing reads, DBG, which provides the double advantage of circumventing the alignment step required by most SNV callers and avoiding additional biases due to the alignment itself.
In theory, MICADo can be used on any kind of sequencing data, however its advantage with respect to other variant calling methods should be lesser in the case of short read datasets with low error ratios. Therefore, MICADo was evaluated on PacBio sequencing datasets: (i) a novel sequencing of TP53 of a breast cancer cohort, (ii) a publicly available dataset of FLT3 sequencing of an acute myeloid leukemia cohort, and (iii) a synthetic dataset.
Here we use an extension of the classical DBG, meaning a colored DBG  (Iqbal et al. , 2012). A colored DBG comprises several read sets and has multi-colored vertices meant to keep track of the datasets to which every k-mer belongs. We build a colored DBG reference graph for the gene reference sequences, including splicing variants and known SNPs. The vertices of this graph are colored according to their original reference sequences. We then build a colored DBG sample graph per patient dataset. The vertices of the sample graph are colored according to the patient dataset, thus with only one color. Second, we compare the reference graph to each sample graph in order to highlight paths in the sample graph that are not present in the reference graph, i. e. , alternative paths, see Figure  1A. Finally, for each alternative path, we verify whether it is specific to the sample or common within the cohort.
TP53 Sequencing Data The p53 protein is encoded by the tumor suppressor gene TP53, the most frequently mutated gene in human cancers  (Olivier et al. , 2010) , given its major implication in response to cellular stress. Most of mutations are found in its DNA Binding Domain used to activate genes involved in apoptosis, growth arrest, or senescence of highly damaged cells  (Brosh and Rotter, 2009). p53 sequencing data is a secondary output of the original EORTC 10994/BIG 1-00 study, registered under NCT00017095 https://clinicaltrials. gov/ct2/show/study/NCT00017095. For the original study and primary data, (see  Bonnefoi et al. , 2011; Iggo et al. , 2013). We have sequenced 48 samples of TP53 transcripts extracted from breast tumor biopsies  (Bonnefoi et al. , 2011)  by PacBio circular sequencing (PacBio RSII; P4-C2 chemistry).
The PacBio sequencing data is available from the NCBI SRA database under the accession number SRP064161 BioProject PRJNA290142. The main interest of this dataset for testing the accuracy of SNV detection is the existence of SNV calling results obtained from 454 Roche sequencing data generated for the 48 samples (available from NCBI SRA database under the accession number SRP020456, BioProject PRJNA193388; see  Iggo et al. , 2013  for details). Moreover, for these 48 samples there exists a classification into three categories (based on the percentage of red colonies in the yeast assay, (see  Bonnefoi et al. , 2011; Iggo et al. , 2013). These categories are: • negative control: 12 samples considered to be wild type; • positive control: 18 samples that are mutated with high rate of altered reads; • difficult group: 18 samples that are mutated with low rate or complex mutations.
FLT3 Sequencing Data The receptor tyrosine kinase FLT3 is highly involved in the development of stem cells and in the immune system  (Gilliland and Griffin, 2002). Mutations of FLT3 have been detected in about 20% of patients with acute myeloid leukemia (AML) and are associated with poor prognosis  (Smith et al. , 2012). This dataset is composed of 19 samples obtained by PacBio circular sequencing of FLT3 transcripts for 8 AML patients before treatment and the same patients after relapse, and 3 healthy individuals with no cancer history. The data is available from NCBI SRA database under the accession number SRP011010 Bioproject PRJNA85103. All patients present an internal tandem duplication (ITD) which are co-located but patient specific and associated after relapse with new point mutations; a complete description of mutations carried by each patient can be found in  Smith et al.
Synthetic Data Synthetic samples with artificially inserted alterations were generated from the pooled TP53 dataset as follows. Approximately 20k reads from all the TP53 samples from the negative control group were first pooled in a single file and mapped with GMAP against the TP53 transcript variant 1 (NCBI Reference Sequence: NM_000546. 5). To build a synthetic sample, we first sample n reads (n ranging from 150 to 1000) from all the mapped reads (chosen with uniform probability), we then sample i positions (i ranging from 1 to 3) to alter between the minimal and the maximal mapped positions. For each of the i alterations, we choose with uniform probabilities between a mismatch, an insertion and a deletion, as well as an alteration length l (l is 1 for mismatches and ranges from 1 to 5 for insertions or deletions).
t. ∃ s ∈ S i , v 1 v 2. v k w k ⊆ s}, and the labeling function l : V → P(L) provides colors for the vertices with L being the set of n labels (colors) corresponding to the n sets of sequences. When it is clear from the context, we will use a shortcut notation v = w for the equality of k-mers that are encoded by the corresponding vertices and omit the notation of the labeling function l. To sum up, in this work we employ the definition of DBG that (i) is extended by a labeling function l to distinguish between reference sequences (such as SNPs and splicing variants) and (ii) explicitely encodes edges corresponding to k-mers present in the sequences. that are truly present in the sequences. Basically this implies that every walk in the graph corresponds to a sequence that exists in at least one dataset and its labels identify the datasets to which the sequence belongs to.
Evaluation on Synthetic Data We evaluated the ability of the three pipelines to recover artificially inserted somatic mutations. We developed to this end a simple reads simulator based on sampling reads from the TP53 dataset. Although, numerous synthetic read simulators for NGS data are available, we opted to use a simple nonparametric sampling-altering scheme based on existing samples in order to preserve as much as possible sequencing biases present within the data. We generated 9245 synthetic datasets by sampling and altering reads from the pool of negative control samples belonging to the TP53 dataset (see Section 2. 2. 3 and Supplementary Section 6 for details). Moreover, any identified variants corresponding to known SNPs (described in Supplementary Section 2) were excluded from this analysis.
The three pipelines were evaluated by computing, for each sample, the number of true positives (number of correctly identified mutations), false positives (number of wrongly identified mutations), and false negatives (number of missed mutations). The results are reported in Figure  4. In terms of precision, GATK and MICADo offer similar performances when more than 10% of the reads are altered. VarScan on the other FIGURE 4 | Comparison of three pipelines on synthetic data. Three pipelines, GATK, MICADo, and VarScan (vertical panels) were run on synthetic data containing from 1 to 3 inserted alterations, harbored by a varying fraction of altered reads (x-axis) among a total of 150, 500, 700, and 1000 reads (horizontal panels). TP-label indicates no True Positives and TP+ indicates that at least one True Positive was identified.
On the contrary, MICADo was run with a lower threshold of 3% minimum variant allele frequency and still adequately identifies both with high precision and recall the majority of mutations even when as few as 3. 5% of the total number of reads contain these mutations. Note that synthetic samples were generated by sampling from the negative control group of the TP53 dataset that were qualified as being "wild-type" by an independent sequencing run as well as by a yeast colony assay test (see Section 2. 2 for details). Therefore, any variant found in the negative control group is either a known SNP or is considered to be a false positive. Consequently, only variants that were inserted by our read simulator were considered as true positives. We then evaluated in more details the ability of MICADo to identify single nucleotide variants and small indels (<5 nt).
FLT3 Targeted Data To further test MICADo, we applied it to the publicly available PacBio sequencing targeted on the FLT3 kinase domain (see Supplementary Section 4). For each patient, we check for absence of mutations in samples before treatment and we search for at least one mutation in samples after relapse in accordance with  Smith et al. (2012). Each sample before treatment and after relapse carries an ITD that are co-localized between each samples but specific to each patient. Normal samples (Normal Control no. 1, no. 2, and no. 3) are not supposed to carry any alterations which was confirmed by MICADo (data not shown). Alteration results obtained by MICADo on the remaining samples were compared with those reported in the original paper, results are shown in Table  2. In the original paper, authors have manually analyzed a small region restricted to only 4 codons downstream from the ITD region.
Our method is especially relevant in the case of clinical targeted PacBio sequencing data for which a generic algorithmic solution has been lacking. MICADo was able to achieve significant improvement over widely used available methods (GATK and VarScan) by controlling both the low false positive and false negative rates. Our analysis also suggests that PacBio targeted sequencing harbors recurrent errors in homopolymer regions -observation that goes counter to the often admitted hypothesis of uniform distribution of sequencing errors in PacBio reads. We believe that our method can be a useful addition to the presently available mutation calling tools and could be effectively used in targeted sequencing with high background noise from cohorts of patients.
Datasets We ran experiments on 5 different corpora: English: Dependencies were extracted from the WSJ part of the Penn Treebank (PTB) with additional NP bracketings  (Vadas and Curran, 2007)  with the LTH converter 9 (default options). Sections 02-21 are used for training, 22 for development and 23 for testing. POS tags were predicted by the Stanford tagger 10 trained with 10jackkniffing. 11  German: We used dependencies from the SPMRL dataset  (Seddah et al. , 2014) , with predicted POS tags and the official split. We removed sentences of length greater than 100 in the test set. Dutch, Spanish and Portuguese: We used the Universal Dependency Treebank 1. 2 (  Van der Beek et al. , 2002; McDonald et al. , 2013; Afonso et al. , 2002)  with gold POS tags and the official split. We removed sentences of length greater than 100 in the test sets.
Those datasets contain different structure distributions as shown in Table  1. Fortunately, our method allows us to easily change the bounded degree constraint or toggle the well-nestedness one. For each language, we decided to use the most constrained combination of bounded block degree constraints and well-nestedness which covers over 99% of the data. Therefore, we chose to enforce 2-BBD and well-nestedness for English and Spanish, 3-BBD and well-nestedness for Dutch and Portuguese and 3-BBD only for German.
Our methods are best fitted for this case. In order to see how much well-nested and bounded block-degree structures are missed by a state-of-the-art parser, we compare our results with TurboParser. 14 We run the parser with three different feature sets: arc-factored, standard (second-order features), and full (third-order features). The results are shown in Table  4. Our model, by enforcing strict compliance to structural rules (100% valid dependency trees), is closer to the empirical distribution than TurboParser in arc-factored mode on all languages but German. Higher-order scoring functions manage to get more similar to the treebank data than our strict thresholds for all languages but Portuguese, at the expense of a significative computational burden. We interpret this fact as an indication that adding higher order features into our system would make the relaxation method converge more often and faster.
SNP-genotyping The 21 FS families and their parents were genotyped with the 20 K Infinium SNP array at the Fondazione Edmund Mach according to published procedures. 28, 37 SNP calling was performed using GenomeStudio software (Illumina Inc. , San Diego, CA, USA; http://www. illumina. com), with a GenCall threshold of 0. 15, and ASSIsT,  43  a filtering and calling pipeline that accounts for the presence of null-alleles and signal intensity differences among AB-genotypes, thus increasing the number of usable SNP markers and providing some fully informative bi-parental markers with segregation type ab × ac.
In view of our mapping effort, this strategy was implemented in the ad hoc developed software Haploblock Aggregator (HapAg-http:// www. wageningenur. nl/en/show/HaploblockAggregator. htm) and applied to our data (Supplementary File S1). For each FS family, HapAg aggregated the segregation information of the SNP markers belonging to the same HB by using the information on linkage group and the linkage phase of the individual markers (Figure  1a ), while considering the meiotic events occurring in the two parental plants separately. Thereto, HapAg splits the parental allelic contribution of every individual of each family into two distinct sub-data sets including either maternal or paternal recombination events (Figure  1b ). Eventually, maternal and paternal data sets of all the progenies from all FS-families were merged to generate a single BC-type data set (Figure  1c ), having twice the number of individuals as the original CP populations (a more detailed description of the methodological steps performed by HapAg is available in the software manual (http://www.
Given the overall estimated genetic to physical size ratio of 548 kbp/cM, based on the results from Velasco and colleagues,  36  the probability of a recombination event occurring within a single HB is expected to be 1. 8 ×10 -4 (FB -HBs), 1. 8 × 10 -3 (IRSC -HBs) and 3. 5 × 10 -4 (FB+IRSC -HBs). These probabilities are so low that we decided to neglect such recombination, at least during the construction of the map, and thus, make missing the HB marker call for the potentially recombinant individual. The final BC-type data set, formed by HB markers and individual SNPs not included in HBs (for simplicity, the generic term 'markers' will be used to refer to both), was used to construct the integrated Genetic Linkage Map (iGLMap).
The presented strategy permits the almost complete exploitation of the segregation information available (losing only some information from the rare recombination events within a HB) while considerably reducing the amount of missing information: in this example, from 76% for the initial CP data sets of the two HBs to 28% in the final unique BC population. For the single SNP, the amount of missing data did not change throughout the process by definition and was 66%. This approach of data aggregation and mating type was implemented in the software Haplotype Aggregator (HapAg-http://www. wageningenur. nl/en/show/HaploblockAg gregator. htm), whose manual describes the process in more detail. The size of the genetic maps in cM (a) and the number of SNP markers mapped (b), are reported for each of the 21 full-sib families, together with average values (± s.
d. ) across families (Pop Mean) and the final integrated Genetic Linkage Map (iGLMap). The table includes some single-parent maps that could not be integrated due to the lack of bi-parental markers (LG6 and LG16 in I_W, and LG7 and LG17 in I_CC). Average marker distance (Avg. dist. ), is estimated as total genetic length divided by the total number of markers, is reported at the bottom of the table. a The parental maps of LG6 and LG17, in family I_W and I_CC, respectively, could not be integrated due to the absence of bi-parental markers and had to be considered separately. Their total length is therefore an estimate, derived by the sum of the paternal maps (cM) to the non-overlapping region of the maternal map, obtained from the alignment of the single-parent maps to the iGLMap. b In family I_CC, the genetic maps of LG7 is given by the maternal parent data only, as the paternal parent did not show any polymorphic marker.
c The top part of the LG16 maternal map in family I_W (LG16P1T) could not be integrated to the entire map due to homozygosity in the paternal LG16 for that region. d For family 12_J, only 2,969 of the 6,351 markers were used in the construction of the iGLMap as a set of 3382 identical markers sensu JoinMap had been overlooked due to an administrative error. The identity of these markers is given in Supplementary File 2, together with their genetic positions on the bi-parental map and their progeny genotypes for family 12_J. S locus (Supplementary Figure  S1c ), thus suggesting the two parents to have a common self-incompatibility allele. Finally, the LGs 13 in families 12_J and 12_K have a genetic length of 1. 5-1. 6x the population mean (Table  2 ). Such relative large sizes are usually an indicator for data issues.
HB marker genotype definition and BC data set The software HapAg was designed in the framework of the current mapping effort to aggregate the segregation information of the SNPs belonging to each HB into a single HB marker. During the aggregation process, HapAg reported 2 848 inconsistencies in SNPs scoring within 748 HBs (26%). More than half of them (51%) resulted from the erroneous assignment of 55 SNPs to a FP and, therefore, to a HB, due to inadequate genome sequence information (unidentified during the mapping effort on single families). Those SNPs were removed from the HBs and used as individual SNP markers (Supplementary Table  S1 ). The remaining 49% of conflicts involved 25% of the HBs and were due to calling errors, recombination within HBs, or gene conversion. The majority of these HBs (96.
Construction of the iGLMap An IFM was constructed using markers that had data for at least 25% of the 3 172 individuals, which was true for 2631 HB and 344 individual SNP markers. The genetic length of its 17 LGs ranged from 64 (LG1) to 113 cM (LG15), for a total IFM length of 1 279 cM (Table  5 ). The IFM was subsequently completed by adding markers with genotypic data for at least 10% of the individuals, to produce the iGLMap. Over the whole mapping process, an improvement in map quality was obtained through the close inspection of 1 320 singletons by graphical genotyping (Supplementary File S3), whereby 67% of the singletons showed to result from 386 misplaced markers. These were moved to nearby positions. The adjusted marker order was then used as the Start Order for map re-estimation verifying that singletons' issues were indeed solved, and no new double recombinants were generated by the shift.