CLC Genomics Workbench 23.0破解版是功能强大的日常生物信息学工作软件包。使用将所有您在生活信息学相关工作所需要的工具集成到友好的软件程序中，并具有强大的可扩展性，能为您快速的生成精准可信的结果，软件使用最先进的技术，融合加速算法，为您带来了一整套的数据分析工具以及丰富的可调整选项和质量控制选项，现在CLC Genomics Workbench 23.0中提供许多期待已久的功能，以帮助您将多样本分析提高到一个新的水平。您现在可以直接从原始序列数据启动工作流程而不需要先进行导入操作，同时在工作流中带来了更多的高级批处理操作和功能，您可以通过多个输入进行批处理，并在同一工作流中进行批处理和聚合。提供结合样本中的报告和十几种专家工具，自动将工作流程中的任何报告以pdf或JSON格式导出，同样在CLC Genomics Server 23.0上，可使用元数据表组织工作流程结果，即使对于大量样本，也可以使您快速找到所需的结果。此外还提供更多功能和改进。 版本性能空前给力，安装包中含破解文件，有需要的朋友不要错过了！
安装破解教程
1、在本站下载并解压，如图所示，得到CLCGenomicsWorkbench_23_0_64.exe安装程序和crack破解文件夹
2、勾选我接受协议，点击next
3、选择软件安装路径，点击next
4、安装完成，退出向导
5、将crack中的文件复制到安装目录中，点击替换目标中的文件，默认路径C:\Program Files\CLC Genomics Workbench 23
新功能介绍
CLC Genomics Workbench Premium 23.0.2
发布日期： 2023-02-13
改进和错误修复
氨基酸变化的运行时间得到了显着改善。
修复了“修剪读取”报告中的问题，即“修剪（断开的对）”的数量不是按作为输入提供的序列列表报告的，而是以增量方式加在一起。报告的“修剪读取”数量相应减少。当修剪来自多个序列列表的成对读取并生成损坏的读取对时，会出现此问题。
修复了一个罕见的问题，如果读取同时根据质量分数和适配器直读进行修整，则可能导致修剪读取保留读取的错误部分。
修复了导致解复用读取工具始终基于“条形码，序列”序列结构进行多重解复的问题。对标记列表的调整（例如添加链接器或将条形码放在末尾）将被忽略。在工作流上下文中运行时，此问题不会影响该工具。
修复了当数据集很大或检测到具有许多可能转录本的融合基因时，可能导致在 Windows 上检测和优化融合基因失败的问题。
修复了导出过滤的空注释轨道时可能导致 VCF 导出失败的问题。
修复了导致 Design Primers 表格中的片段长度不正确的问题。打开表格时会重新计算这些值，因此不需要重复以前的设计。
修复了导致 QIAseq xHYB 病毒面板参考数据集在 Windows 上下载失败的问题。
Fixed a rare issue where Rebuild Index could not repair a corrupt search index.
Various minor bug fixes
齐根CLC基因组学工作台 23.0.1
发布日期： 2023-01-17
改进和错误修复
Fixed an issue affecting Trim Reads, where the wrong part of a read was retained if the read was both trimmed to a fixed length and also trimmed by another method from the opposite end of the read.
Fixed an issue affecting Trim Reads when both adapter trimming using a trim adapter list and fixed length trimming were selected. This issue could cause the resulting trimmed reads to be shorter than expected.
Fixed an issue where fusion plots created by Detect and Refine Fusion Genes were omitted in the report and were not accessible via the fusion track table.
Fixed an issue where workflows containing a Branch on Coverage element would fail for read mappings with no zero coverage regions when using reports output by QC for Read Mapping.
Fixed an issue where dates indicated with forward slashes in CSV format files were not recognized as dates by Import Metadata.
Fixed an issue where the history entry in a sequence list after sorting always stated the sorting was based on length, even the sorting was based on name or marked status.
Fixed an issue causing Annotate with GFF/GTF/GVF file to fail when the option “Ignore duplicate annotation” was checked.
Fixed an issue causing Standard Import of GenBank format to stall if qualifier names spanned more than one line.
Various minor improvements
请参阅下面的 CLC 基因组学工作台 23.0 的发行说明，了解自此软件上一个常规版本以来更改的完整列表。
QIAGEN CLC 基因组学工作台 23.0
发布日期： 2023-01-17
新工具
Homology Based Cloning – Design cloning experiments for cloning methods relying on homologous ends, such as Gibson Assembly
.
Create K-medoids Clustering for RNA-Seq finds clusters of features, e.g., genes/transcripts/miRNAs etc, whose expressions behave similarly, for example first increasing over time and then decreasing. The tool produces a Clustering Collection which contains a Sankey plot showing how these features move between clusters under different conditions, for example different treatments. A line graph representation of features from individual clusters or pairs of clusters is present as well.
来自插件的新工具
Detect and Refine Fusion Genes – Find fusion genes in RNA-Seq data by identifying potential fusions and then refining that list by evaluation of the evidence for each fusion. This is an updated version of the tool formerly distributed in the Biomedical Genomics Analysis plugin. The updates made are listed in an Improvements section below.
Target Region Coverage Analysis – Analyze and compare coverage from multiple samples. This tool was formerly distributed in the Biomedical Genomics Analysis plugin.
Create Consensus Sequences from Variants – Create consensus sequences from a variant track and a reference sequence. This tool was formerly distributed in the Biomedical Genomics Analysis plugin.
Annotate with GFF/GVF/GTF file – Add annotations from a GFF, GVF or GTF format file onto sequences, individual or in sequence lists. This tool was formerly distributed in the Annotate with GFF file plugin.
其他新功能和改进
RNA-Seq 分析工具
New tutorial: Get hands-on experience with new RNA-Seq analysis functionality, including Create K-medoids Clustering for RNA-Seq (see New Tools above), with the RNA-Seq analysis with four tissues and six timepoints tutorial.
Improvements to RNA-Seq Analysis:
Substantial speed improvements. Reads that map to multiple transcripts or genes will be distributed differently than earlier due to different choices of random seed in the new implementation. The algorithm is still deterministic.
Transcripts are no longer renamed in Transcript Expression (TE) output unless renaming is necessary to avoid duplicate names. Previously, transcripts were renamed to the gene name plus a number e.g. “BRCA_1”. This change means that TE tracks in this version of the software cannot typically be used together with TE tracks generated using older versions to produce Heat Maps, PCA plots, Expression, etc.
Reports UMI fragment counts when relevant. UMI counts are included in the Fragment statistics section of the report if the input reads are annotated with UMIs by tools from the Biomedical Genomics Analysis plugin, and if the library type is set to 3′ sequencing for RNA-Seq Analysis.
Improvements to Heat Maps:
Samples can be ordered by the Tree, Sample, or Active metadata layer options, or any individual metadata entry.
Optimize tree layouts – a new option for reordering features to produce a top-left to bottom-right diagonal.
The order of the metadata categories can be adjusted. This order is reflected in the legend.
Metadata categories are alphabetically sorted.
The Expression Browser includes a new plot for visualizing genes across samples and contrasts and metadata categories are sorted alphabetically.
Venn diagrams support four and five groups. Previously up to 3 were supported. Tooltips indicate which groups are part of a specific intersection.
PCA plots produced by PCA for RNA-Seq:
Have two table views. The first table view shows the loadings of the principal components. The second table view shows the coordinates of the points.
The order of the metadata categories in 2D PCA plots can be adjusted. This order is reflected in the legend.
miRNA 分析工具
Quantify miRNA:
Handles custom databases containing duplicated names.
Does not allow custom databases containing sequences longer than 60bp. This avoids misallocation of reads to sequences that are similar to small RNAs.
When adding multiple inputs to Extract IsomiR Counts, the extracted expression tables contain an entry for the combined set of IsomiRs identified among the samples, making them compatible for analysis in Differential Expression in Two Groups and Differential Expression for RNA-Seq.
两组RNA-seq的差异表达和差异表达
A new option for creating a subset has been added to the miRNA Statistical Comparison Table produced by Differential Expression for RNA-Seq and Differential Expression in Two Groups.
It is possible to downweigh outliers. This option is disabled by default and recommended only when the results seem enriched for genes that are expressed at anomalously high levels in a small proportion of samples.
The Max Group Means column of Statistical Comparison Tracks and Tables now shows TPM instead of RPKM. Note that this column is used for filtering data in tools such as Create Heat Map for RNA-Seq and the Pathway Analysis tool of the Ingenuity Pathway Analysis plugin.
检测和完善融合基因
这是检测和优化融合基因的更新版本，以前分布在生物医学基因组学分析插件中。此处列出的更新与生物医学基因组学分析 22.2 一起分发的版本相关。
Fusions will not be called for overlapping genes.
Novel exon boundary improvements:
Options have been expanded to allow for detecting fusions with a single fusion partner (“Detect with novel exon boundaries”) as well as detecting those with 2 fusion partners (“Allow fusions with novel exon boundaries in both genes”)
The “Detect exon skippings” option supports detection of fusions with novel exon boundaries.
An option has been added to omit non-significant breakpoints from the report.
A minimum Z-score can now be specified for use when evaluating evidence for a fusion.
Speed improvements
The option “Allow fusions with novel exon boundaries in both genes” now defaults to false to reduce the number of false positive fusions. Setting it to true is useful for exhaustive searches of novel fusions.
Changes to the maximum number of equivalent matches to the reference allowed for a single read to be retained:
When remapping reads to a fusion chromosome, the maximum number is now 30. Previously it was 10.
When searching for unaligned ends, the maximum number remains unchanged, as 10.
The option “Maximum number of hits for a read” has been removed. It’s value was ignored in previous versions.
Fusions from mRNA transcripts without an associated gene in the Gene track are not used when detecting fusions. mRNA transcript features must have a gene id in one of the following columns to be matched with the associated gene: “Parent”, “gene_id” or “gene_name”.
Fixed an issue where paired end reads were treated as single end reads when the option to “Only use fusion primer reads” was enabled.
Fixed an issue where unaligned ends could be too long or too short for reads containing insertions and deletions. This change may lead to small differences in results compared to earlier versions, expected to be due to a decrease in false positive and false negatives reported.
亚硫酸氢盐作图
Map Reads to Bisulfite Reference speed improvement. This is data dependent, with about a 50% improvement likely for most data sets. This speed up might change the details of results very slightly.
Call Methylation Level speed improvement. This speedup might, in some cases, change results very slightly.
Import of read mappings from SAM/BAM now use methylation information from the optional SAM tags XR for read conversion and XG for reference conversion. The recognized values are “CT” and “GA”. Support for these tags is added so that information is not lost if a bisulfite mapping is exported and then re-imported.
Export of read mappings to SAM/BAM format now includes details on bisulfite conversion. These are specified using the SAM tags XR for read conversion and XG for reference conversion. The possible values of these tags are “CT” and “GA”. This is provided for increased compatibility with third party tools.
工作流程
Branch on Coverage – a new workflow control flow element where the downstream processing of read mappings can be controlled based on coverage values within reports.
Import with Metadata – new template workflow that imports sequence data into sequence lists and associates the imported elements to a CLC Metadata Table containing descriptive information for each sample.
Workflows containing Demultiplex Reads elements and workflows containing Split Sequence List elements can be run in Batch mode.
Barcodes can be preconfigured in Demultiplex Reads elements in workflows.
Workflow Export elements can be preconfigured to export to locations on AWS S3.
When Annotate with Overlap Information is included more than once in the same workflow, columns with overlap information are now always added in the same order. Previously, concurrency issues could cause column order to be different between different runs.
在 SRA 中搜索读取
Technical reads can be downloaded in addition to biological reads. The reads to import, as well as the read structure and orientation are configurable.
When multiple accessions are provided in an Accession query field, each is searched for separately. Previously only entries containing all the accessions entered were returned.
An estimate of the disk space and final size of imported sequence lists is no longer provided in the wizard, but further information about space requirements has been added to the manual.
A troubleshooting section has been added to the manual.
读取映射
Read mapping speed on Apple Silicon processors has been improved. Read mapping results are not affected by this. Tools benefiting from this change include Map Reads to Reference, RNA-Seq Analysis, Map Reads to Contigs and Map Bisulfite Reads to Reference.
In stand-alone read mappings and read mapping tracks, deletions are now highlighted in the coverage graph and in the shown reads.
For stand alone read mappings a “Match coloring” side panel provides the colors applied to reads when the compactness level is set to “Packed”.
导入和导出
VCF Import:
Supports symbolic alleles for inversions (<INV>), insertions (<INS>), deletions (<DEL>) and tandem duplications (<TANDEM:DUP>). Symbolic alleles that do not contain sequence information or are longer than 100,000 base pairs are imported to annotation tracks instead of variant tracks. Previously symbolic alleles were not imported.
Improved handling of variants with multiple loci encoded in the same vcf record.
VCF Export supports symbolic allele representation for insertions (<INS>), deletions (<DEL>) and tandem duplications (<TANDEM:DUP>). (Inversions (<INV>) were already supported.) With the exception of deletions, variants in annotation tracks are always exported as symbolic alleles. Deletions in annotation tracks and variants in variant tracks above a specified size are also exported as symbolic alleles. The default size is 1000 bp, which corresponds with the QCI Interpret requirement that InDels > 1000 bp must be represented as symbolic alleles.
The PacBio importer supports HiFi reads.
The read length when exporting to FASTQ format files has been increased from 524,288 bp to 16,777,216 bp.
SAM/BAM Mapping Files importer:
Performance improvements
The circular flag of references is now retained.
Import Tracks from File has been updated to show a warning if the file is not imported.
GFF3 Export retains the case of attribute headers. Previously, all headers were adjusted to lower case during export.
The history information of elements imported using Standard Import includes the specific importer used (e.g. “CSV table importer”, “Fasta Importer”, etc).
Standard Import can be used to import files from AWS S3 locations.
When exporting images to bitmap-based formats, the Screen resolution and High resolution options are now bounded so the maximum supported number of pixels will not be exceeded.
序列列表
Checkboxes can be enabled to select sequences within the graphical view of sequence lists. Lists can be sorted based on whether they are marked or not, and marked sequences can be deleted.
In the Annotation Table view, the following changes have been made to the right-click menu:
The underlying sequence of selected annotations can be deleted.
Names of sequences selected annotations are on can be copied to the clipboard.
The option to export to gff now exports to GFF3 format – Export Selected to GFF3 File. This option has also been updated in the Annotation Table view of individual sequence elements.
In the Table view, selected sequences can be deleted, and the names of selected sequences can be copied.
Various minor improvement to labels in right-click menus.
CLC 元数据表
When launching analyses in Batch mode, or when launching workflows with an Iterate element, CLC Metadata Tables with data associated can be used directly as input. Each row in the CLC Metadata Table is a batch unit, with data elements associated to a row, of a type compatible as input to the analysis, being the default contents of a batch unit. When launching workflows, the column to base the batch units on can be specified.
New options for editing CLC Metadata Tables, including for adding content from other CLC Metadata Tables or Excel, CSV or TSV files. Rows in a CLC Metadata Table can also be selected and used to make a new CLC Metadata Table.
When associating data automatically to CLC Metadata Tables, a preview of the associations that will be made is shown in the wizard.
图1。 “迭代”和“收集和分发”控制元素允许在工作流的各个部分进行批处理。在此示例中，可以在单个工作流程中分析三次重复进行的二级因子RNA-seq实验的fastq文件。修剪读数，进行质量控制（QC'ed），并逐个样品绘制RNA-seq分析读数。然后比较各组之间的RNA-seq表达水平，并收集比较结果以创建热图，维恩图和PCA图。最后，将修整，质控和RNA-seq分析读取的作图报告合并到各个样品中。该工作流程用于分析De Maio等人的数据。（2016），比较登革热病毒2和模拟感染的人类细胞在感染后24和36小时的转录谱（RNA-seq）。
3、工作与智慧的元数据：使用元数据作为通用和方便的方式来帮助你在工作台内组织你的样品和结果。元数据可以帮助您找到对象，分批定义输入分组，将样品定向到工作流中的不同路径（例如，在肿瘤正常或三重研究中）或用于统计分析和可视化RNA序列。您只需要开始一个Excel电子表格即可。快速获取所需结果，即使是大批样品也是如此。现在，元数据表可以组织工作流程结果，因此您可以快速找到所需的答案。
4、自动从工作流程中以pdf或JSON格式导出报告：使用JSON格式的结果，高级用户可以以编程方式解析报告并创建自定义报告，并将其CLC工作流完全集成到现有系统中。直接从QIAGEN CLC Genomics Workbench 20.0或QIAGEN CLC Genomics Server 20.0的pdf或JSON格式的工作流中以pdf或JSON格式（图1，深蓝色元素）导出报告和组合报告，并且可以选择包括用于文件来源的历史记录日志。
5、合并报告：执行多步骤工作流并按多个样本进行批处理时，每个步骤和每个样本都会创建多个报告，这种情况会迅速产生信息过载。使用新的“合并报告”工具，将报告和跨工具和样品的结果结合起来，快速概览关键的质控参数和主要结果，该工具与高级批处理功能完全兼容（图1，浅蓝色工作流元素和示例输出报告如图2）。支持来自20多种与NGS相关的工具（包括生物医学和微生物工具）的报告，以及各种变体的统计信息。
图2. 使用“合并报告”工具，您可以快速概览分析中的主要结果。在这种情况下，从De Maio等人的12个RNA-seq样品的QC报告中总结了GC含量。（2016）。
6、QIAGEN CLC Genomics Workbench 20.0和QIAGEN CLC Genomics Server 20.0还对许多工具进行了更新，并且与以前的版本相比，性能得到了显着改善。您可以在此处获得最新改进的完整列表。
7、通过安装功能丰富的模块和插件，可以将其他内容添加到QIAGEN CLC Genomics Workbench 20.0和QIAGEN CLC Genomics Server 20.0中的综合工具箱中。免费的生物医学基因组学分析插件和QIAGEN CLC微生物基因组学模块均已针对此版本进行了实质性更新（请参见下文）。
二、QIAGEN CLC主工作台：
1、新的“生物分子生成器”工具使基于PDB文件中的对称性信息生成或提取生物分子成为可能。
2、使用新的“查找和模型结构”工具，只需两个步骤即可创建序列的同源性模型。该工具从蛋白质数据库（PDB）中识别合适的蛋白质模板，并针对给定的输入序列自动构建结构模型。从结果表中，可以一键创建序列的结构模型。
3、分子项目中的分子结构可以导出到PDB文件。
4、这些新工具适用于QIAGEN CLC Main Workbench和QIAGEN CLC GenomicsWorkbench。您可以在此处获得最新改进的完整列表 。
三、QIAGEN CLC Genomics Server：
1、引入了新的工作流排队选项，以便可以在多节点环境中高效地执行利用高级批处理功能的工作流。
2、本博客中提到的其他QIAGEN CLC产品现在可用的所有新功能也适用于QIAGEN CLC Genomics Server。
四、QIAGEN CLC微生物基因组学模块：
1、随着全基因组测序彻底改变了临床微生物学，微生物基因组的MLST正迅速成为标准。QIAGEN CLC微生物基因组学模块现在不仅包括cg / wgMLST，还包括用于爆发分析的MLST 的交互式最小生成树可视化功能（图3）。该工具还提供对具有国际公认模式的pubMLST.org和其他在线公共数据库的直接访问。总而言之，这些工具为研究人员提供了一套分析分离物的全面灵活性，无论它是病毒，细菌还是真菌基因组。您可以在此处获得最新改进的完整列表 。
图3. QIAGEN CLC微生物基因组学模块产生的最小生成树。
2、生物医学基因组分析插件：
QIAGEN CLC Genomics Workbench现在通过可通过Biomedical Genomics Analysis插件访问的一系列新的即用型工作流程，支持更多基于QIAseq UMI的文库制备试剂盒和面板，包括：
该QIAseq多模式面板可用于单工作流程解决方案的支持。
所述QIAseq融合XP面板都支持，包括变体通话，融合检测和表达定量。
在hg19和hg38中轻松分析QIAseq MSI Booster Panel –提供了新的MSI工作流程，可以为多个样品创建共享基线。
该QIAseq甲基化面板，QIAseq甲基库套件现已支持，其中包括差异甲基化级呼叫。
其他改进包括：改进了QIAseq 3'UPX解决方案的报告和解复用功能，使用新的可视化功能更好地检测了基因融合，并通过以VCF格式导出CNV和融合调用结果，将融合和CNV调用与QCI Interpret集成。您可以在此处获得最新改进的完整列表 。
软件优势
1、先进的配料变得简单
您的研究可能会产生许多样本，但我们会支持您的分析。 即使使用了许多工具，工作流也使分析大批次产品变得容易，并且可以为许多样品产生结果。
您可以使用任意数量的输入来批处理工作流：只需在要选择的每个输入选择的输入选择步骤中勾选“批处理”框。
对于跨样本分析，您现在可以在单个工作流程中进行批处理和汇总。 只需将批处理部分放在“迭代”和“收集和分发”元素之间即可。
2、使用元数据更智能地工作
使用元数据以方便的方式在工作台中组织样本和结果，并享受多功能性。 使用元数据查找对象来定义批处理中的输入分组，将样本定向到工作流中的不同路径（例如，在肿瘤正常或三重研究中），或用于RNA-Seq的统计分析和可视化。 您只需要开始一个Excel电子表格即可。
3、工作台也是免费的查看器
为了确保您可以无缝协作，我们确保所有人都能查看由工作台或插件生成的任何数据。 要求您的同伴下载Workbench试用版，并在需要时安装插件，即使在试用期结束后，他们也始终可以免费看到您的结果。
软件特色
1、适合所有人的基因组学工作台
CLC Genomics Workbench是一个功能强大的解决方案，适用于每个人，无论工作流程如何。利用尖端技术，独特功能和算法，工业界和学术界的科学领导者广泛使用该技术来克服与数据分析相关的挑战。
2、全面的NGS数据分析
用户友好的生物信息学软件解决方案可对您的NGS数据进行全面分析，包括从头开始组装整个基因组和转录组，重新测序分析（WGS，WES和目标面板支持），变异调用，RNA序列，ChIP序列和DNA甲基化（亚硫酸氢盐测序分析）。
使用易于使用的转录组学工作流程分析您的RNA-seq和小RNA（miRNA，lncRNA）数据，以便在基因和转录本水平进行差异表达分析。
揭示微生物群，其基因组和宿主之间的关键相关性。通过用于基因发现，注释，分类学和功能性微生物组分析的工具和简化的分析工作流程，可以轻松了解复杂的宏基因组学数据。另外，请使用完整的流行病学工具集和抗菌素耐药性数据库。
受支持的NGS平台是Illumina，IonTorrent，PacBio和GeneReader。
3、生物医学基因组学分析
生物医学基因组学分析和面板数据分析功能通过CLC Genomics Workbench和免费插件生物医学基因组学分析提供。该插件提供了用于NGS面板数据分析，WES，WGS和RNA-seq的工具和工作流程。
4、即用型工作流程
具有针对人类，小鼠和大鼠基因组的完整参考集的生物医学工作流程，包括遗传性疾病工作流程（三重分析，四口之家）和肿瘤学体细胞突变检测工作流程，可用于FFPE或液体活检样品（单个样品或肿瘤样本）。正常匹配的样本）。这些工作流程能够灵敏地检测SNP，MNV，InDel，串联重复序列，结构变异，融合基因和CNV。包括带有保守评分的注释以及dbSNP和ClinVar上的过滤步骤。
5、用插件扩展工具箱
CLC Genomics Workbench提供了多个插件，可针对特定应用进行量身定制，例如多序列比对，全基因组比对，转录本发现，生物医学基因组学分析和微生物基因组学。这些功能丰富的扩展已无缝集成到CLC Genomics Workbench中，并提供了高级工具和工作流，可满足您对分析的特定需求。
6、不要相信我们的话：听听客户的意见
CLC Genomics Workbench已为成千上万的科学文章做出了贡献，并且是迄今为止针对分子生物学家和生物医学专业人士引用次数最多的科学软件之一。
7、提高您的生产力是我们的业务。
CLC Genomics Workbench允许缩放到任意大小。企业解决方案允许整个机构使用相同的软件。
8、定制的工作流程可加速您的数据分析
CLC基因组学工作台的开发旨在支持各种NGS生物信息学应用。工作流可以将质量控制步骤，适配器修整，读取映射，变体检测以及多个过滤和注释步骤组合在一起，形成一个管道，您可以与同事共享并单击即可执行。
新的工作流程元素以及增强的元数据功能允许在工作流程内进行批处理，例如对迭代许多RNA-seq样本的量化然后进行组的统计比较很有用。
9、使用QIAGEN Digital Insights从样本到洞察
通过CLC Genomics Workbench，您可以轻松访问许多物种的预格式化参考数据集。您还可以将结果提交给QIAGEN Digital Insights产品组合中的其他解决方案，例如，机能路径分析（IPA），机能变异分析（IVA）和QIAGEN Clinical Insights Interpret（QCI）。
软件功能
1、重新排序
CLC Genomics Workbench支持完整的重测序流程，用于检测和比较遗传变异。当处理大量样品时，高效的算法可减少运行时间，而可定制的分析工作流程和批处理可将动手时间缩短到最小。
CLC Genomics Workbench允许您专注于对检测到的变异的生物学解释。
2、读取映射
重新排序的第一步是准确的读取映射。我们的算法经过优化，可以快速，高效地存储大量数据，以进行高质量的映射。
该算法为各种数据格式提供了全面的支持，包括短读取和长读取，以及成对读取数据的所有形式，无论插入大小或读取方向如何。它还支持使用混合数据集。局部重排可以大大降低某些变体类型的假阳性检测率。我们的目标是减少您的手工工作，并专注于从原始NGS读数中获得生物学上有意义的结果。
3、转录因子ChIP-Seq
即使在没有对照样品的情况下，最新的改进也有望对转录调控有更深入的了解。转录因子ChIP-seq暴露了整个基因组中转录结合位点的特征峰区域。通过我们久经考验且受信任的交互式图形用户界面，所有这些都可以访问。在我们的表观基因组学解决方案中发现更多信息。
4、亚硫酸氢盐测序
胞嘧啶甲基化可能是表观遗传调控基因表达的最佳研究形式。用于CLC基因组学工作台的Bisulfite测序插件可为科学家在研究胞嘧啶甲基化数据时面临的常见问题提供一站式解决方案。详细了解我们的表观基因组学功能。
5、工作流程
工作流将所选工具简化为一项分析。工作流可以批量运行，使其成为功能强大的工具，可在最少的用户干预下处理大量样品。您可以按照自己的科学出版物轻松地设置自己的工作流程，并与同事或其他研究人员创建并共享工作流程安装程序文件。商业附加组件引入了预配置的示例工作流程，以使您从数据到发布的速度更快。
6、从头组装
我们倍受赞誉的de novo汇编器，并带有修整工具，可删除低质量的数据，从而快速提供汇编质量并有效地利用资源。就像我们的读取映射器一样，支持多种NGS数据类型，并且混合程序集结合了短读取和长读取的独特优势，从而获得了最佳结果。如果长读段的组装（例如PacBio）或基因组整理是主要重点，那么可以使用CLC Genome Finishing Module增强CLC Genomics Workbench 。
7、RNA序列
免费的“ Advanced RNA-Seq插件”将所有分析步骤（从读数的二次分析到复杂的统计数据）集成到易于使用的工作流程中，并提供了从案例控制或多重分析等多种实验设计的访问权限组实验到多因素实验。所有工具都考虑了由于测序深度而引起的差异，从而无需对输入数据进行标准化。用于批次效应的多因素统计控制，并支持配对研究。统计结果可以在基因组环境中显示为轨迹，表视图或通过许多利用元数据的可视化选项进行可视化：火山图，二维热图，主成分分析和维恩图。
8、组蛋白ChIP-Seq插件
组蛋白的乙酰化是与染色质去浓缩和基因表达上调或下调相关的重要的表观遗传适应。用于CLC基因组学工作台的Histone ChIP-Seq插件可检测基因或其他预定义基因组区域中Histone乙酰化标记的证据。详细了解我们的表观基因组学功能。
9、变异分析
CLC Genomics Workbench提供了一系列准确的变体检测器，可检测单核苷酸变体（SNV），多核苷酸变体（MNV），中小型插入，缺失或替换以及拷贝数变体（CNV）和其
