AbstractDespite the growing variety of sequencing and variant-calling tools, no workflow performs equally well across the entire human genome. Understanding context-dependent performance is critical for enabling researchers, clinicians, and developers to make informed tradeoffs when selecting sequencing hardware and software.

摘要尽管测序和变异调用工具的种类越来越多，但没有一个工作流程在整个人类基因组中表现得同样好。了解上下文相关的性能对于研究人员，临床医生和开发人员在选择测序硬件和软件时做出明智的权衡至关重要。

Here we describe a set of “stratifications,” which are BED files that define distinct contexts throughout the genome. We define these for GRCh37/38 as well as the new T2T-CHM13 reference, adding many new hard-to-sequence regions which are critical for understanding performance as the field progresses.

在这里，我们描述了一组“分层”，它们是BED文件，定义了整个基因组的不同背景。我们为GRCh37/38以及新的T2T-CHM13参考文献定义了这些区域，增加了许多新的难以测序的区域，随着领域的发展，这些区域对于理解性能至关重要。

Specifically, we highlight the increase in hard-to-map and GC-rich stratifications in CHM13 relative to the previous references. We then compare the benchmarking performance with each reference and show the performance penalty brought about by these additional difficult regions in CHM13. Additionally, we demonstrate how the stratifications can track context-specific improvements over different platform iterations, using Oxford Nanopore Technologies as an example.

具体而言，我们强调了相对于以前的参考文献，CHM13中难以映射和富含GC的分层增加。然后，我们将基准测试性能与每个参考进行比较，并显示CHM13中这些额外困难区域带来的性能损失。此外，我们以牛津纳米孔技术为例，演示了分层如何在不同的平台迭代中跟踪特定于上下文的改进。

The means to generate these stratifications are available as a snakemake pipeline at https://github.com/usnistgov/giab-stratifications. We anticipate this being useful in enabling precise risk-reward calculations when building sequencing pipelines for any of the commonly-used reference genomes..

产生这些分层的方法可以作为蛇形管道在https://github.com/usnistgov/giab-stratifications.我们预计，在为任何常用的参考基因组构建测序管道时，这将有助于实现精确的风险回报计算。。

IntroductionThe last few decades have brought a vast array of increasingly-powerful sequencing platforms and associated software to read DNA molecules. However, no tool or pipeline performs equally across all genomic contexts within the human genome. Particularly difficult genomic contexts include large duplications and large repeats.

引言过去几十年来，带来了大量越来越强大的测序平台和相关软件来读取DNA分子。然而，没有任何工具或管道在人类基因组内的所有基因组环境中表现相同。特别困难的基因组环境包括大重复和大重复。

Additionally, many sequencing platforms have relatively low performance in homopolymers, and platforms that perform better in homopolymers use short-reads which lack the mapping advantage long reads have in large repeats. The mappers and variant callers used to analyze reads from these platforms also bring context-specific performance implications due to the assumptions (implicit or explicit) they often make when processing sequencing data1.

此外，许多测序平台在均聚物中的性能相对较低，而在均聚物中表现更好的平台使用短读段，这缺乏长读段在大重复序列中的映射优势。由于在处理测序数据时经常做出的假设（隐式或显式），用于分析这些平台读数的映射器和变体调用者也会带来特定于上下文的性能影响1。

Therefore, improving and fully utilizing the sequencing landscape will require detailed analysis of how different tools perform in a given genomic context.To this end, we previously developed “genome stratifications” which are carefully-defined browser extensible data (BED) files that divide the human genome into meaningful contexts for benchmarking.

因此，改进和充分利用测序环境将需要详细分析不同工具在给定基因组背景下的表现。为此，我们之前开发了“基因组分层”，这是精心定义的浏览器可扩展数据（BED）文件，可将人类基因组划分为有意义的背景以进行基准测试。

The genomic stratifications were originally developed in collaboration with the Global Alliance for Genomics and Health (GA4GH)2 and are being further developed by the Genome in a Bottle Consortium (GIAB). Coding regions, low mappability regions, high GC content regions, and various types of repetitive regions are examples of such genomic stratifications, and these are currently defined with regard to two linear references, GRCh37 and GRCh38.

基因组分层最初是与全球基因组学与健康联盟（GA4GH）2合作开发的，目前正在由Genome in a Bottle Consortium（GIAB）进一步开发。编码区，低可映射性区，高GC含量区和各种类型的重复区是这种基因组分层的例子，目前这些是关于两个线性参考文献GRCh37和GRCh38定义的。

These stratifications are designed to be used with benchmarks such as those developed by GIAB, which generates variant benchmarks for a set of human genomes to enable development, optimization, evaluation, and compari.

。

FTBL: https://ftp.ncbi.nlm.nih.gov//genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_feature_table.txt.gz

FTBL：https://ftp.ncbi.nlm.nih.gov//genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_feature_table.txt.gz

GFF: https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_genomic.gff.gz

GFF：https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_genomic.gff.gz

Additionally, the script required a .fai index file which was created from the CHM13v2.0 reference assembly.Generating GC content BED files using seqtk for CHM13v2.0We use an existing script created to generate the GRCh38 GC Content Stratification BED files. The script required seqtk version-1.3-r106 tool, bedtools v2.27.1, and tabix v1.9.

此外，该脚本需要一个从CHM13v2.0引用程序集创建的.fai索引文件。使用seqtk for CHM13v2.0生成GC内容床文件我们使用创建的现有脚本来生成GRCh38 GC内容分层床文件。该脚本需要seqtk版本1.3-r106工具、bedtools v2.27.1和tabix v1.9。

Three essential data files were required to run the script file: the CHM13v2.0 FASTA, the CHM13 genome file. The genome was converted to BED format by adding a middle column of 0 (such that each line had the length of the entire chromosome). We ran seqtk for various fractions of GC content, all within windows of 100 bp.

运行脚本文件需要三个基本数据文件：CHM13v2.0 FASTA，CHM13基因组文件。通过添加0的中间列（使得每条线具有整个染色体的长度），将基因组转换为BED格式。我们对GC含量的各个部分运行了seqtk，所有这些都在100 bp的窗口内。

After running seqtk, we added 50 bp slop to each BED file and merged.Lift-over for OtherDifficult regionsIn order to find the coordinate of well-studied genes including MHC, KIR, and VDJ that are considered as difficult regions, we performed liftover for such regions from GRCh38 to CHM13v2.0. To obtain the OtherDifficult regions data of the GRCh38 we referred to the reference sample released by the GIAB https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.1/GRCh38/OtherDifficult/.

运行seqtk后，我们向每个BED文件中添加了50 bp的斜率并进行了合并。提升其他困难区域为了找到被认为是困难区域的经过充分研究的基因（包括MHC，KIR和VDJ）的坐标，我们对从GRCh38到CHM13v2.0的这些区域进行了提升。为了获得GRCh38的其他困难地区数据，我们参考了GIAB发布的参考样本https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.1/GRCh38/OtherDifficult/.

To perform the lift-over, we used the minimap2 (v2.24) aligner with arguments -ax asm5 followed by bedtools bamtobed and merge (v2.30.0). The resulting BED files are provided as part of the GIAB stratification resource.Snakemake pipelineOverviewThis work (first done as part of a hackathon) was incorporated into a snakemake pipeline which can be found at https://github.com/usnistgov/giab-stratifications-pipeline and https://github.com/usnistgov/giab-stratifications.

为了执行提升，我们使用了带有参数的minimap2（v2.24）对齐器-ax asm5，然后是bedtools bamtobed和merge（v2.30.0）。产生的BED文件作为GIAB分层资源的一部分提供。Snakemake管道概述这项工作（最初是作为hackathon的一部分完成的）被整合到Snakemake管道中，可以在https://github.com/usnistgov/giab-stratifications-pipeline和https://github.com/usnistgov/giab-stratifications.

The latter repository holds the global configuration for the three references in this work, and references the former repository as a submodul.

后一个存储库保存了这项工作中三个引用的全局配置，并将前一个存储库作为子模块引用。

Only contained valid chromosomes (i.e., 1-22, X, Y).

仅包含有效的染色体（即1-22，X，Y）。

File was bgzip compressed.

文件已被bgzip压缩。

File was a valid BED file (three columns, tab-delimited, with 2nd and 3rd columns as non-negative integers with 3rd greater than 2nd).

文件是有效的BED文件（三列，制表符分隔，第二列和第三列为非负整数，第三列大于第二列）。

All regions in the BED file were sorted in numeric order (i.e., chromosomes ordered 1-22, X, then Y with each region then sorted by start and end).

BED文件中的所有区域均按数字顺序排序（即染色体顺序为1-22，X，然后Y，每个区域然后按开始和结束排序）。

No regions overlapped with each other.

没有区域相互重叠。

No region overlapped a gap region (which included the PAR on chromosome Y)

没有区域与间隙区域重叠（其中包括Y染色体上的PAR）

No region fell outside chromosomal boundaries.

。

Evaluating the utility of stratifications for benchmarkingWe created an assembly-based benchmark from the Q100 assembly for HG002. Specifically, the HG002 Q100 small variant benchmark was created using v0.011 of DeFrABB (https://github.com/usnistgov/giab-defrabb), the T2T-HG002-Q100v1.0 diploid assembly (https://github.com/marbl/hg002), and GRCh38 reference (https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_HG002_DraftBenchmark_defrabbV0.011-20230725/).DeFrABB (Development Framework for Assembly-Based Benchmarks) is a snakemake-based pipeline created to facilitate the iterative development of benchmarks sets for evaluating variant callsets using high-quality diploid assemblies (https://github.com/usnistgov/defrabb).

评估分层对基准测试的效用我们从Q100组件为HG002创建了一个基于组件的基准。具体来说，HG002 Q100小变体基准测试是使用DeFrABB的v0.011创建的(https://github.com/usnistgov/giab-defrabb)，T2T-HG002-Q100v1.0二倍体组件(https://github.com/marbl/hg002)，以及GRCh38参考(https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_HG002_DraftBenchmark_defrabbV0.011-20230725/)。DeFrABB（基于程序集的基准测试开发框架）是一个基于蛇形图的管道，旨在促进基准集的迭代开发，以使用高质量的二倍体程序集评估各种调用集(https://github.com/usnistgov/defrabb)。

DeFrABB first generates assembly-based variant calls using dipcall v0.3 (https://github.com/lh3/dipcall)44. Dipcall was run with default parameters with the following Z-drop parameter, -z200000,10000,200, which yielded more contiguous assembly-assembly alignments compared to the default value. After reformatting and annotation, the variant set reported by dipcall (VCF) was used as the draft benchmark variants.

DeFrABB首先使用dipcall v0.3生成基于程序集的变体调用(https://github.com/lh3/dipcall)使用默认参数运行Dipcall，并使用以下Z-drop参数-Z20000010000200，与默认值相比，它产生了更多连续的组件-组件对齐。重新格式化和注释后，dipcall（VCF）报告的变体集被用作基准测试变体草案。

Note that we call these “draft” variants since this benchmark has not been officially evaluated and released by GIAB yet; however, GIAB and the Telomere to Telomere Consortium have polished and curated the assembly and variant calls sufficiently for it to be used for this analysis.The benchmark regions (analogous to the “confident regions” in the GIAB v4.2.1 small variant benchmarks) are defined as regions with a 1:1 alignment between each assembled haplotype and the reference (except chromosomes X and Y).

请注意，我们称这些“草案”变体，因为该基准尚未由GIAB正式评估和发布；然而，GIAB和端粒到端粒联盟已经对组装和变异调用进行了充分的修饰和策划，以便将其用于此分析。基准区域（类似于GIAB v4.2.1小变异基准中的“置信区域”）定义为每个组装单倍型与参考（X和Y染色体除外）之间具有1:1比对的区域。

These regions excluded gaps in the assembly and their flanking sequences, as well as any large repeats (sat.

这些区域排除了组装中的缺口及其侧翼序列，以及任何大的重复序列（sat）。

Data availability

数据可用性

All versions of the genome stratifications up to v3.5 (the latest as of this writing) are available on an FTP site hosted by NCBI here at https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/.

所有版本的基因组分层都可以在NCBI托管的FTP网站上找到，最高版本为v3.5（本文撰写时的最新版本）https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/.

Code availability

代码可用性

The initial work for this study (which originally took place at a hackathon) is freely available https://github.com/collaborativebioinformatics/NIST-GREX. The preliminary version of the code to generate stratifications is available at https://github.com/genome-in-a-bottle/genome-stratifications. The full pipeline in snakemake is available at https://github.com/usnistgov/giab-stratifications.

这项研究的初步工作（最初是在一次黑客竞赛中进行的）可以免费获得https://github.com/collaborativebioinformatics/NIST-GREX.生成分层的代码的初步版本可在https://github.com/genome-in-a-bottle/genome-stratifications.snakemake的完整管道可在https://github.com/usnistgov/giab-stratifications.

A copy of the GitHub repository and HTML output of the snakemake pipeline are archived at Zenodo at https://zenodo.org/records/11176260..

GitHub存储库的副本和snakemake管道的HTML输出存档在Zenodohttps://zenodo.org/records/11176260..

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。bioRxiv 2020.12.11.422022。https://doi.org/10.1101/2020.12.11.422022（2020年）。下载参考文献致谢我们感谢Sierra Miller和Katherine Gettings的反馈。确定了某些商业设备、仪器或材料，以充分规定实验条件或报告的结果。

Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the equipment, instruments, or materials identified are necessarily the best available for the purpose.Author informationAuthor notesThese authors contributed equally: Sina Majidian, Justin M.

这种识别并不意味着国家标准与技术研究所的推荐或认可，也不意味着所识别的设备、仪器或材料一定是用于该目的的最佳可用材料。作者信息作者注意到这些作者做出了同样的贡献：Sina Majidian，Justin M。

Zook.Authors and AffiliationsMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD., USANathan Dwarshuis, Jennifer McDaniel, Nathan D. Olson, Justin Wagner & Justin M. ZookHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USADivya Kalra & Fritz J.

佐克。作者和附属机构马里兰州盖瑟斯堡国家标准与技术研究所材料测量实验室，USANathan Dwarshuis，詹妮弗·麦克丹尼尔，Nathan D.奥尔森，贾斯汀·瓦格纳和贾斯汀·M·佐科曼基因组测序中心，贝勒医学院，休斯顿，德克萨斯州，USADivya Kalra和Fritz J。

SedlazeckUniversity of Applied Sciences Upper Austria - FH Hagenberg, Hagenberg im Mühlkreis, AustriaPhilippe SanioCenter for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USAPilar Alvarez JerezDepartment of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UKPilar Alvarez JerezDepartment of Genetics and Genomic Sciences and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount, Hess Center for Science and Medicine, New York, NY, USABharati JadhavDepartment of Computer Science, College of E.

。

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PubMed Google ScholarContributionsN.D., F.J.S, J.W., S.M., and J.M.Z designed the study. N.D. implemented the pipeline. N.D., D.K., J.M., N.D.O, P.S, P.A.J, B.J., E.H., R.M. and S.M. performed the analyses. N.D., B.B., F.J.S, S.M., and J.M.Z organized the study. All authors reviewed and approved the manuscript.Corresponding authorsCorrespondence to.

PubMed谷歌学术贡献。D、 ，F.J.S，J.W.，S.M。和J.M.Z设计了这项研究。N、 D.实施管道。N、 D.，D.K.，J.M.，N.D.O，P.S，P.A.J，B.J.，E.H.，R.M.和S.M.进行了分析。N、 。所有作者都审查并批准了手稿。通讯作者通讯。

Sina Majidian or Justin M. Zook.Ethics declarations

Sina Majidian或Justin M.Zook。道德宣言

Competing interests

相互竞争的利益

F.J.S. receives research support from Genetech, Illumina, ONT and Pacbio. B.B. is a full-time employee of DNAnexus. The remaining authors declare no competing interests

F、 J.S.获得Genetech、Illumina、ONT和Pacbio的研究支持。B.B.是DNAnexus的全职员工。其余作者声明没有利益冲突

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Reprints and permissionsAbout this articleCite this articleDwarshuis, N., Kalra, D., McDaniel, J. et al. The GIAB genomic stratifications resource for human reference genomes.

转载和许可本文引用本文Drawhuis，N.，Kalra，D.，McDaniel，J。等人，人类参考基因组的GIAB基因组分层资源。

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