dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of top-down CO 2 emission estimates with inventory data Figure  9  shows our annual estimates of the total CO 2 emissions from biomass burning in Siberia (in the selected region indicated in Fig. 2a ) in comparison to corresponding estimates obtained with the data from the GFASv1. 0 (Kaiser et al. , 2012) and GFED3. 1 (van der Werf et al. , 2010)  global biomass burning emission inventories. Our estimates were obtained for several years (2007-2012) by using the FRPto-BBR conversion factors optimized with the data for the period from 1 May to 30 September 2012 and applied to the period from April to September of each year. The gridded CO 2 emission data from the GFASv1. 0 and GFED3. 1 inventories were integrated over the same region and period as our emission estimates. Unfortunately, the GFED3.
0 and GFED3. 1 inventories can be explained by the above-mentioned calibration of the FRP-to-BBR conversion in the GFASv1. 0 inventory by using the data of the GFED3. 1 inventory. In spite of this calibration, the spatial distributions of the CO 2 emission fields calculated in the two inventories can be regarded as being sufficiently independent from each other. The intercomparison of the spatial distributions of the CO 2 emission estimates obtained in this study and calculated with the GFASv1. 0 and GFED3. 1 inventory data for the year 2008 is illustrated in Figs. 10  and 11. While all the distributions (see Fig. 10 ) look, in general, rather similar, there are considerable irregular differences not only in magnitudes but also in the locations of fires. In particular, many grid cells exhibit noticeable emissions according to the GFASv1.
The scatter plots of the different gridded emission estimates (see Fig. 11 ) show that the differences between the emissions attributed to a given grid cell in the different inventories frequently reach several orders of magnitude (note that only grid cells with emissions larger than 10 -4 g CO 2 m -2 are depicted in the plots and reflected in the statistics). Along with irregular discrepancies between the estimates, there are also some differences that have a systematic character (apart from the differences in the mean values). In particular, grid cells with relatively small emissions (less than 1 g CO 2 m -2 ) in our data are typically assigned (relatively) much larger values in the GFASv1. 0 inventory. This is, likely, a result of the application of the data assimilation procedure, which in the GFASv1.
0 inventory efficiently smoothes out strong temporal variations in the FRP densities. This kind of systematic difference between our estimates and the GFASv1. 0 data is scarcely visible when these estimates are compared with the data of the GFED3. 1 inventory: both the GFASv1. 0 inventory and our method yield systematically larger values for the grid cells in which CO 2 emissions evaluated by the GFED3. 1 are less than about 1 g CO 2 m -2. This fact is in line with the above remark about a possibly stronger sensitivity of FRP measurements to fire activity, compared to the burnt area measurements. In spite of substantial "random" differences between these estimates, there are also considerable correlations between the emission fields. Rather surprisingly, correlation of our estimates with the GFED3.
1 data (r ∼ 0. 71) is larger than with the GFASv1. 0 data (r ∼ 0. 66). This shows that the differences in the data processing algorithms in the situation considered here are at least as important as the differences associated with the different nature of input data. The correlation between the GFASv1. 0 and GFED3. 1 data is weakest (r ∼ 0. 64). The strong correlation of our data with both these independent data sets suggests that our estimates are quite robust, and indicates that the overall uncertainties in the spatial distribution of our CO 2 emission estimates are, at least, not larger than the overall uncertainties in the spatial distributions of the GFED3. 1 and GFASv1. 0 data for Siberia.
Summary and conclusions This paper presents a general method for the estimation of CO 2 emissions from open biomass burning by using satellite measurements. Effectively, the method is based on (1) deriving emissions of some trace species (gases or aerosols) co-emitted with CO 2 by inverting their observations with a chemistry-transport model and (2) rescaling the emissions of those species to the CO 2 emissions by using literature data for emission factors. Using satellite measurements of two (or more) different species in the framework of the proposed method enables cross-validation of the emission parameters inferred from observations of the different species and constraining of uncertainties in the optimal CO 2 emission estimates. As a source of initial information on the spatial structure and temporal variations of the biomass burning rate (BBR) and pyrogenic emissions, the method employs satellite measurements of the fire radiative power (FRP).
The spatiotemporal fields of FRP were obtained from the respective MODIS measurements. The diurnal variations of FRP were evaluated by using the same FRP data consistently with estimates of the daily mean FRP values involved in our parameterization of the CO 2 emission rates. Note that the emission factors for aerosol, CO, and CO 2 employed in our analysis were not evaluated this study, but taken from the literature. It is found that the optimal values of the FRP-to-BBR conversion factors derived from the AOD measurements are larger (by factors of about 2-3) and more uncertain than those derived from the CO measurements. This difference (which may be due to, e. g. , underestimation of aerosol emis-sion factors) is consistent with underestimation of aerosol emissions reported in the literature  (Kaiser et al.
0 emission database (the GFED3. 1 data for 2012 were not available). Considerable differences were also revealed between our estimates and the inventory data for other years (specifically, our indirect "top-down" estimates for the total biomass burning CO 2 emissions in the period from 2007 to 2011 in Siberia are by the factors of 1. 8 and 2. 5 larger than the corresponding alternative estimates), although all of the estimates demonstrate rather similar inter-annual variability. Comparison of the spatial structures of the CO 2 emission estimates obtained in this study and provided by the GFASv1. 0 and GFED3. 1 emission inventories revealed that the correlation of our estimates with the results of both inventories is better than the correlation between the GFASv1. 0 and GFED3. 1 estimates. We consider this outcome as evidence that the overall uncertainties in our CO 2 emission estimates for Siberia do not exceed the uncertainties in the respective GFED3.
Acknowledgements. Partial support for this study was provided by the  Russian Foundation for Basic Research  (grant no. 14-05-00481-a ) and the  Russian Academy of Sciences  in the framework of the  Programme for Basic Research "Electrodynamics of atmosphere; Electrical Processes, Radiophysical Methods of Research ". The CO measurements at ZOTTO are made in cooperation with the  Sukachev Institute of Forest, Krasnoyarsk , and funded by the  Max Planck Society. J. W. Kaiser  was supported by the  FP7 EU  project  MACC-II  (grant agreement no. 283576 ). Edited by:  M. Kopacz.
The emergence of next generation DNA sequencing technology is enabling high-resolution cancer genome analysis. Large-scale projects like the International Cancer Genome Consortium (ICGC) are systematically scanning cancer genomes to identify recurrent somatic mutations. Second generation DNA sequencing, however, is still an evolving technology and procedures, both experimental and analytical, are constantly changing. Thus the research community is still defining a set of best practices for cancer genome data analysis, with no single protocol emerging to fulfil this role. Here we describe an extensive benchmark exercise to identify and resolve issues of somatic mutation calling. Whole genome sequence datasets comprising tumor-normal pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, were shared within the ICGC and submissions of somatic mutation calls were compared to verified mutations and to each other.
Introduction The International Cancer Genome Consortium (ICGC) is characterizing over 25,000 cancer cases from many forms of cancer. Currently there are 74 projects supported by different national and international funding agencies. As innovation and development of sequencing technologies drove prices down and throughput up, projects transitioned from exome to whole genome sequencing (WGS) of tumor and matched germline samples, supplemented by transcript and methylation analyses when possible. These studies have discovered new biology for many different forms of cancer. Interestingly, even though WGS is used for many of these studies, the analyses to date have focused on protein-coding exons that represent only 1-2% of the genome. Data and results produced by ICGC partners are deposited in a common database (https://dcc.
icgc. org/). The ICGC has established working groups to empower cross-learning among the various projects and their staff. It became apparent early on that there were marked differences in how teams generated WGS data and analyzed it. In order to understand some of the issues emanating from different sequencing and analysis approaches, the ICGC Verification Group was created and several cross-centre comparisons, called benchmarks (BM), were launched. Based on cost, capacity and analytical experience it was determined that comprehensive identification of tumor-specific somatic mutations requires WGS with a minimum of 30x sequence coverage of the tumor and normal genomes  11. Currently, next generation sequencing generates billions of short reads of ~100 bases in length per sample (read length is increasing), which are then typically aligned to a reference human genome.
Differences between tumor and normal genomes are identified as Somatic Single-base Mutations (SSM), Somatic Insertion/deletion Mutations (SIM) and structural changes (rearrangements and chromosome segment copy number changes). Conceptually this seems like a simple task, however, cancer genome analysis is challenging and subject to multiple pitfalls. The ICGC Technology Working group's goal is to explore the various analytical methods and develop a set of best practices to share with the research community. How somatic mutation calling is done is crucial and key to the quality of the results that are produced and deposited in the ICGC database. More importantly, we argue that dissemination of this knowhow beyond the research community and its adoption by health care organizations will be a necessary step toward turning high-resolution cancer genome profiling into a robust clinical tool.
We also received 10 structural variant submissions. Verification of mutation calls and generation of a "Gold Set" was carried out after the first 14 submissions. Two independent target capture experiments were carried out (Online Methods) using as input all somatic mutations (SSM and SIM) called by at least two submitters and up to 500 private SSM calls per submitter from the set of 14 submissions received by that time (see Supplemental Table  1 ). The target design, capture and sequencing were done at two different sites (University of Oviedo, Spain and OICR, Canada) on an IonTorrent PGM and an Illumina MiSeq, respectively, using the same input DNA that was used for the generation of the WGS data. To establish a Gold Set of mutations for CLL the two Haloplex enrichment datasets were merged with the original Illumina sequencing data.
We received 18 SSM and 16 SIM submissions for BM1. 2 that were analysed the same way as the submission for BM. 1. 1. Four submitters to BM1. 1 did not participate in BM1. 2 (CLL. R, CLL. S, CLL. T and CLL. U), and there were four new submitters who had not participated in BM1. 1 (MB. H, MB. J, MB. M, and MB. Q). For verification of the medulloblastoma mutation calls (BM1. 2), we used a 300x dataset from BM2 to generate a Gold Set (Supplemental Methods). In brief, the 300x dataset was analysed using six different somatic mutation calling pipelines. Agreement of at least 3 pipelines was deemed as a positive (a subset of 10% was reviewed manually to confirm this). All calls made by only two or one pipelines were inspected manually and attributed to the tiers outlined above. The MB Gold Set had a total of 1645 bona fide mutation calls across all tiers (Table  1 ), with 962, 1101, 1255, and 1267 SSMs in Tiers 1, 2, 3 and 4, respectively, and 337 and 353 SIMs in Tiers 1 and 4, respectively.
In another analysis we tried to determine genomic features such as repeat features as well as some key sample/sequence/alignment features that created problems for callers. Membership (Boolean flags) or scores (for continuous characteristics) were tabulated for CLL and MB SSMs and SIMs (Supplemental Tables  2 3 4 5 ). The frequencies or mean scores, respectively, were computed for three subsets of each submission (true positives, false positives, or false negatives) and for the Gold Sets. In order to highlight problematic features for each submission, the differences with respect to the Gold Set were computed and multiplied by the false positive or false negative rate, accordingly. The problematic features of false positives in the MB SSM dataset, for which the verification sequence was more uniformly distributed over all candidate mutation loci than that of CLL, are shown as a heat map (Figure  6 ).
D. The differences between sets of mutations submitted by the participating groups (Figures  1  and 2 ) raised questions about the impact of individual pipeline components on the results. The extent of observed pipeline customization (Supplemental Note and Supplemental File 1) did not allow for exhaustive testing of all potentially important analysis steps (even less so with various the parameter settings), but three pipeline components were selected for a closer inspection due to their expected high impact: mapper, reference genome build and mutation caller. Four mappers (Novoalign2, BWA, BWA-mem and GEM), two SSM callers (MuTect and Strelka) and three versions of the human reference genome (b37, b37+decoy and "hg19r" -a reduced version of hg19, with its unplaced contigs and haplotype chromosomes removed) were selected for testing, based on their usage by the benchmarking groups (see Supplemental Note for software versions and settings).
In order to limit the effect of non-tested software on the produced mutation sets, a simple SSM-calling pipeline was established. First we compared the effect of the mapper with each of the SSM callers. With a single SSM caller employed, a considerable fraction of unfiltered SSM calls for a given mapper (0. 22 to 0. 69, depending on the mapper-caller combination), were not reproducible by that caller with any other mapper (Supplemental Figure  25 ). When compared to the Gold Set (Tier 3 SSMs), calls supported by a single mapper were almost exclusively false positives (FP) (precision < 0. 02). On the other hand, a large majority of calls supported by all four mappers were true positives (with precision ranging from 0. 87 for MuTect to 0. 99 for Strelka). Similar trends could be observed when SSM callers were compared while holding the mapper constant (Supplemental Table  6 ).
We found that dominating features for difficulties both for false positives and false negatives calls for SSMs were low coverage in the tumor, and aberrant coverage of tumor and normal, while for SIM calling repetitive features such as inTRs and sRep tend to dominate both in CLL and MB. We also detected distinct clustering of different analysts. Even though analysts used similar combinations of software in their pipelines, very few similarities were detected in their calls. The combination of software is not as critical as how each piece of software is applied and what settings are applied. A slight correlation of true positive SSM calls of pipelines using MuTect and Strelka can be seen (Figure  4 ). Data analysis pipelines are constructed by integrating software from diverse sources. In many instances the approaches used in the different software and the assumptions made therein are not evident to the user and many parts are black boxes.
So, having a benchmarking dataset is of great importance. The sequencing reads and Gold Sets of BM1. 2 are available to the research community through the ICGC DACO to benchmark their own pipelines. In conclusion, we have conducted a thorough benchmark of mutation calling in cancer genomes. The two tumor types we selected are particular in that they present a comparatively low number of SSMs and SIMs and no structural rearrangements, and the tumor samples we chose had very high tumor cellularity, in the case of the CLL tumor due to cell sorting. The MB tumor added a degree of difficulty to the analysis in that this tumor presented a large degree of tetraploidy combined with other changes of ploidy in chromosomes 1, 4, 8, 15 and 17. As general advice for somatic mutation analysis in cancer genomes we would suggest to start with gaining a basic understanding of nature of the tumor and tumor-type that is being analysed.
Good tools to determine these features prior to the detection of SSMs and SIMs are needed. Resolving issues such as the one that we have elaborated in this benchmark is critical to take cancer sequencing adoptable for the clinic. coverage of elevated GC-content regions of the genome. The normal and GC enriched libraries were sequenced on Illumina GAIIx (2x150 bp) and Illumina HiSeq2000 (2x100 bp) instruments. The same amount of data (~40x coverage) was produced for tumor and corresponding normal sample with the proportions of the two library types -600 million reads for the standard and 200 million reads for the GC enriched library. Reads in FASTQ format were generated using the RTA software provided by Illumina. Verification by target capture and orthogonal sequencing technologies 4080 SSMs and 883 SIMs were selected for Haloplex target capture.
Each sample was sequenced using the Illumina MiSeq platform with 2 x 150 base reads. 3. 3 and 5. 0 million reads were obtained covering the Haloplex products for the reference and tumor samples, respectively. Greater than 80% of the original 4,674 input targets were sequenced at over 20x coverage. For verification of somatic mutations using IonTorrent, a HaloPlex custom capture was designed to enrich a total of 5179 mutations (4492 substitutions and 687 small indels) with a target region of 100 bp centered on the mutation position using the SureDesign software (Agilent). Sequencing was performed using two runs of the IonTorrent 418 chip, resulting in a total of more than 5 and 8 million reads for the tumor and normal samples, respectively. All 40x reads (GAIIx and HiSeq2000 reads that were provided to the participants), MiSeq and IonTorrent verification reads were aligned with GEM (gem-mapper) and converted to BAM format.
Alignments were filtered to retain only primary alignments with mapping quality >= 20. Duplicates were removed with Picard, indels realigned at 1000 genomes indel target locations, and indels were left-aligned using GATK. The pileups at SSM positions were extracted using samtools mpileup with base quality threshold >= 13. Read depth and base counts were extracted using a custom script. Mutant allele and normal counts were compared using in-house software snape-cmp-counts  13  which compares alternate and reference allele counts in tumor and normal, and then scored according to the probability that they are derived from different beta distributions. Mappabilities with 0, 1 and 2% mismatches were computed for the reference genome (h37d5). The average mappabilities in 100bp windows preceding each candidate mutation were stored as tracks for visualization in IGV.
Whole genome sequencing The sequencing reads for BM1. 2 were produced using a no-PCR library preparation procedure that was adapted from the KAPA Library Preparation Kit protocol used together with Illumina TruSeq adaptors and omitting PCR amplification, each for the tumor and the corresponding normal DNA sample. For each sample two libraries were prepared with smaller (roughly 300 bases) and larger (roughly 450 bases) insert size. Sizing was done by agarose gel separation and excision of corresponding size bands. The four libraries were sequenced to 40x using Illumina HiSeq2000 (2x100 bp) and 4x using Illumina MiSeq (2x250 bp). Reads in fastq format were generated using the RTA software provided by Illumina.
Verification by 300x coverage For verification of the submissions of BM1. 2 all reads produced for BM2. 0 were combined and analysed to generate a curated set of results (Gold Set). The combined sequences gave roughly 300x coverage of the tumor and the normal. Six different teams carried out mutation calling using their pipelines (different combinations of aligners, mutation callers and filters). A consensus set was generated accepting all calls made by more than three submitters. All calls made by three or fewer submitters were reviewed manually. IGV screenshots of the mutation positions, juxtaposing the normal and the tumor were generated automatically using a script developed at the DKFZ (Ivo B. please describe with some detail). The images were shared as an image library on Google+ and scored by eight reviewers manually by visual inspection.
For calls that did not achieve complete agreement with the reviewers, a final decision was reached by visualizing the contentious mutation with different aligners, computing the mappability of the local region, and comparing the normal and mutant allele counts using in-house software (snape-cmp-counts), as was done above for BM1. 1. The only major differences in procedure were 1) that high depth across a candidate position was also criteria for rejection and assignment to class 5, 2) multiple pipelines were used to call mutations, and 3) the candidate mutations were subjected to more manual scrutiny by the analysis team. As for BM1. 1, all calls were classified according to the following criteria (Table  1 ): Class 1) A somatic call that is supported by >10% of the reads in the 300x set, Class 2) a somatic call that is supported by 5%-10% of the reads in the 300x set, Class 3) a somatic call that is supported by <5% of the reads in the 300x set.
The Jaccard index is defined as the intersection divided by the union. Tandem repeats were annotated with two programs. Tandem repeat finder (trf) was run on 201bp windows around each SSM and SIM call. Any repeats greater than or equal to six repeat units overlapping or immediately adjacent to the mutation position was annotated accordingly. SGA was also used to annotate homopolymers specifically, giving a richer annotation of the repeat context and change induced by the mutation. Correspondence analysis was performed using the 'ca' package in R on a table where each corresponds to a genomic position at which at least one submission calls a somatic mutation in the Gold Set. The columns comprise the presence or absence in each call set, and Boolean values indicating whether certain genomic features such as repeats or presence in a blacklisted region apply, as well as sequence data such as allele frequency or depth.
Rainfall plots represent the distance for each SSM call from its immediately prior SSM on the reference genome. For each submission, the SSM set used was made of SSM called classified as True Positive or False Positive and SSM from the Gold Set Tier 4 that were absent from the submission, classified as False Negative positions. Feature analysis was done as follows. Tandem repeats were annotated with two programs. Tandem repeat finder (trf) was run on 201bp windows around each SSM and SIM call. Any repeats greater than or equal to six repeat units overlapping or immediately adjacent to the mutation position was annotated accordingly. SGA was also used to annotate homopolymers specifically, giving a richer annotation of the repeat context and change induced by the mutation. Mappability was calculated using the GEM package with one mismatch at both 100mer and 150mer lengths.