5月week3 文献阅读：BloodSpot: a database of gene expression profiles **and transcriptional programs for healthy and **malignant haematopoiesis

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ABSTRACT

• Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation.

  对人类和小鼠造血的研究已经产生了大量的基因表达数据集，这些数据集有可能回答关于正常和异常血液形成的问题。

• To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely in- accessible.

  对于生物信息学经验有限的研究人员和临床医生来说，这些数据仍然可用，但在很大程度上是不可获取的。

• Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival.

  目前的数据库提供有关基因表达的信息，但未能回答有关共同调控、基因程序或对患者生存影响的关键问题。

• To address these short- comings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles fromFACS sorted healthy and malignant haematopoietic cells.

  为了解决这些不足，我们提出了BloodSpot (www.bloodspot.eu)，它包括并大大扩展了我们之前发布的数据库HemaExplorer，这是一个从facs中筛选健康和恶性造血细胞的基因表达谱数据库。

• A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan–Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database.

  修改后的交互界面同时提供了一个基因表达图、Kaplan-Meier分析和描述数据库中不同细胞类型之间关系的层次树。

• The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool.

  该数据库目前包括23个高质量的与正常和恶性血液形成相关的数据集，此外，我们还组装并构建了一个独特的集成数据集BloodPool。

• Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia.

  血池包含了超过2000个样本，这些样本来自于6项关于急性髓系白血病的独立研究。

• Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well- defined haematopoietic cellular space.

  此外，我们还设计了一个健壮的样本集成程序，允许在用户一个定义良好的造血细胞空间中对用户自己提供的患者样本进行敏感比较。

  (背景，当前数据库的缺陷，此数据的功能特点以及数据特点)

INTRODUCTION

• A decade of intense studies of the genetic programs underlying normal and malignant haematopoiesis has resulted in a number of gene-expression data sets, which can potentially help answer questions concerning the molecular mechanisms governing normal haematopoiesis and how these are de-regulated in cancer.

  对正常和恶性造血的遗传程序进行了十年的深入研究，获得了许多基因表达数据集，这可能有助于回答有关控制正常造血的分子机制以及这些机制在癌症中是如何被解除调控的问题。

• To researchers and clinicians with limited bioinformatics experience, these data have been available through online databases in the form of raw or semi-processed files but remained largely inacces- sible for analysis, let alone comparison with user-supplied in-house data.

  对于生物信息学经验有限的研究人员和临床医生来说，这些数据以原始或半处理文件的形式通过在线数据库提供，但在很大程度上仍然无法进行分析，更不用说与用户提供的内部数据进行比较了。

• Recently, a number of web interfaces have been generated to facilitate single gene queries of in-house data (ImmGen Gene Skyline (1), Gene-expression Atlas (2), Leukemia Gene Atlas (3) and Differentiation Map (2)) or curated, compiled and processed data sets (HemaEx- plorer (3), Gene Expression Commons (4), A HeamAtlas (5), BloodChIP (6), BloodExpress (7) and CODEX (8)).

  最近,许多web界面生成促进单基因内部数据的查询(ImmGen Gene Skyline(1)基因表达图谱(2)白血病基因图谱(3)和分化地图(2))或策划,编制和处理数据集(HemaEx——”(3),Gene Expression Commons(4),A HeamAtlas (5), BloodChIP (6), BloodExpress(7)和CODEX 8))。

• These tools provide information on the expression of single genes, but fail to answer the main questions as to whether these genes influence patient survival or if genes or pathways are regulated in similar or inverse patterns.

  这些工具提供了关于单个基因表达的信息，但无法回答这些基因是否影响患者生存，或者基因或通路是否以类似或相反的模式被调控等主要问题。

• We have previously published a comprehensive database of mRNA microarray samples from FACS sorted healthy and leukemic bone marrow samples (3) which has proven a useful and popular resource for researchers working within the areas of cellular differentiation, haematopoiesis and leukaemia

  我们之前发布了一个完整的mRNA微阵列样本数据库，这些样本来自于FACS分类的健康和白血病骨髓样本(3)，已经被证明是细胞分化、造血和白血病领域研究人员的有用和流行的资源

（当前单个基因表达的信息的数据库的缺陷）

• Here, we present a complete overhaul and significantly expanded version of the original database, with a new and interactive interface, all freely available online.

  在这里，我们将对原始数据库进行全面的修改和显著扩展，并提供一个新的交互式界面，所有这些都可以在网上免费获得。

• The new database redefines current approaches to explorative data integration, presentation and visualisation of gene- expression in the haematopoietic system.

  新的数据库重新定义了当前的方法，探索数据集成，表达和可视化的基因表达在造血系统。

• Consequently, all these improvements called for a new name: BloodSpot.

  因此，所有这些改进都需要一个新名字: BloodSpot。

（提出新的数据库：BloodSpot，以及此数据库特性）

• The core function of BloodSpot is to provide an expression plot of genes in healthy and cancerous haematopoietic cells at specific differentiation stages.

  BloodSpot的核心功能是在特定分化阶段为健康和癌变造血细胞提供基因表达图谱。

• The core function of BloodSpot is to provide an expression plot of genes in healthy and cancerous haematopoietic cells at specific differentiation stages.

  血斑的核心功能是在特定分化阶段为健康和癌变造血细胞提供基因表达图谱。

• To present these haematopoietic gene profiles, we have developed a novel visualization chart that simply integrates the benefits of stripcharts and violin plots.

  为了展示这些造血基因图谱，我们开发了一个新的可视化图表，它简单地集成了条形图和小提琴图的优点。

• The server accepts either a unique gene name (gene alias) or a gene signature name from the MSigDB database.

  服务器接受来自MSigDB数据库的唯一基因名(基因别名)或基因签名名。

• Of note, an auto-complete mechanism helps finding the right names for genes and gene signatures.

  值得注意的是，自动完成机制有助于为基因和基因签名找到正确的名称。

• To contextualise the haematopoietic gene expression profile, two additional levels of visualisation are available: an interactive hierarchical tree that shows the relationship between the samples displayed and a Kaplan–Meier plot based on a high-quality Acute Myeloid Leukemia (AML) data set (9). Additionally, we added a large body of curated data sets to the database, which users can query seamlessly.

  放到造血的基因表达谱,另外两个级别的可视化是可用的:一个互动的分层树显示样本关系,kaplan meier点图之间的关系基于一个高质量的急性髓系白血病(AML)数据集(9)。此外,我们添加了大量的策划数据到数据库,用户可以查询无缝。

• Significantly, we provide a new integrated data set of samples from AML patients along with FACS sorted samples from healthy individuals.

  值得注意的是，我们提供了一个新的AML患者样本的综合数据集，以及来自健康个体的FACS分类样本。

• This new integrated data set provides the most detailed picture of the gene expression landscape in healthy and malignant haematopoiesis to date.

  这个新的综合数据集提供了迄今为止健康和恶性造血中基因表达的最详细的图景。

• Finally, the database provides the possibility of comparing user-supplied leukaemia samples to healthy cells.

  最后，该数据库提供了将用户提供的白血病样本与健康细胞进行比较的可能性。

（BloodSpot的特性）

• The platform is freely available, and requires no login, at: www.bloodspot.eu

  该平台是免费的，不需要登录，网址:www.bloodspot.eu

DATA CONTENT UPDATES

Available data sets

• BloodSpot is a database of mRNA expression in healthy andmalignant haematopoiesis and includes data from both humans and mice.

  BloodSpot是一个健康和恶性造血中mRNA表达的数据库，包括来自人类和小鼠的数据。

• The database is sub-divided into several data sets that are each accessible for browsing through the new interface.

  数据库被细分为几个数据集，每个数据集都可以通过新接口进行浏览。

• Data sets are organised by organism of origin and disease status.

  数据集由生物体的起源和疾病状态来组织。

• The data sets are organised as follows: first, human healthy haematopoietic cells, then human leukaemia and finally healthy mouse haematopoietic cells.

  数据集的组织如下:首先，人类健康的造血细胞，然后是人类白血病，最后是健康的小鼠造血细胞。

• BloodSpot contains the data sets from our previous HemaExplorer (3) as well as new published data sets, all manually processed as described in Rapin et al. (10).

  BloodSpot包含我们以前的HemaExplorer(3)和新发布的数据集，所有这些数据集都是按照Rapin等人(10)的描述手动处理的。

• For completeness, the database also includes the content of other online databases that we deem relevant for the study of haematopoiesis in the framework of BloodSpot.

  为了完整，该数据库还包含了我们认为在BloodSpot框架下与造血研究相关的其他在线数据库的内容。

• These ex- ternal databases include the Differentiation Map (DMAP) (2) and the Immunological Genome project (ImmGen) (1).

  这些外部数据库包括分化图(DMAP)(2)和免疫基因组计划(ImmGen)(1)。

  （BloodPool 包含的数据集以及数据集处理方法）

• In total the platform encompasses more than 5000 samples (see Tables 1–3).

  该平台总共包含5000多个示例(见表1-3)。

• All data sets were controlled for qual- ity, appropriately normalised and adjusted for batch effects when necessary (11,12)

  对所有数据集进行质量控制，在必要时进行适当的标准化和批量效果调整(11,12)。

  （数据总包括的样本，以及数据处理方法）

  
  table1

table2 table3 Normalisation method

BloodPool

• One new feature of BloodSpot is BloodPool, an aggregated and integrated data set grouping the results of multiple studies focusing on AML.

  bloodspot的一个新特性是血池，这是一个聚合和集成的数据集，将多个关注AML的研究结果进行分组。

• By means of our batch correction methods this data set can be used to study gene expression (programs) in AML in comparison with healthy corresponding cells (see Figure 1). Using the computational method developed in Rapin et al. (10), we have also computed gene expression fold changes relative to their nearest normal counterparts for all AML profiles in BloodPool.

  通过我们的批处理校正方法这个数据集可用于研究基因表达(程序)在AML与健康相比,相应的细胞(见图1)。使用计算方法为Rapin 等人(10)开发的,我们也计算基因表达倍数变化相对于最近的正常同行在BloodPool AML的表达。

• BloodPool is available for browsing within BloodSpot and can be selected as any of the other available data sets.

  BloodPool可以在BloodSpot内浏览，并且可以作为任何其他可用数据集进行选择。

fig1A fig1B fig1

MSigDB and CMAP gene signatures integration

• We collected all gene signatures available from the Molecular Signatures Database (MSigDB) (13) (version 4.0) (http: //www.broadinstitute.org/gsea/msigdb/) and computed, for each signature, the mean expression values for all samples in all data sets.

  我们从分子签名数据库(MSigDB) (13) (version 4.0) (http: //www.broadinstitute.org/gsea/msigdb/)收集了所有可用的基因签名，并计算了所有数据集中所有样本的平均表达值。

• These mean values summarise the expression of a signature for each sample.

  这些平均值概括了每个样本的签名表达式。

• Connectivity map(CMAP) (13) signatures were generated with the rank matrix provided by the database.

  使用数据库提供的秩矩阵生成连通性映射(CMAP)(13)签名。

• For each combination of compound and concentration, we reported the top and bottom 500 genes and produced gene signatures.

  对于每种化合物和浓度的组合，我们报告了前500和后500个基因，并产生了基因签名。

• The data displayed in BloodSpot represent the mean value of all genes in a given signature.

  血斑中显示的数据代表了给定签名中所有基因的平均值。

Data normalisation

• All data were normalised and batch corrected to eliminate potential lab batch effects.

  所有的数据都进行了标准化和批次校正，以消除潜在的实验室批次效应。

• For this we performed Robust Multiarray Average (RMA) (14) normalisation of all mi-croarray .CEL data files partitioned by origin, and next applied ComBat (http://jlab.byu.edu/ComBat/)(12) an empirical Bayes method implemented in the R language.

  为此，我们对所有按原始分区的mi-croarray . cel数据文件进行了鲁棒多阵列平均(RMA)(14)标准化，然后应用于战斗(http://jlab.byu.edu/ComBat/)(12)用R语言实现的经验贝叶斯方法。

• The batches were defined to be the study name/number, while the covariates was assigned to the relevant cell type.

  批次被定义为研究名称/编号，而协变量被分配到相关的细胞类型。

• The resulting integrated gene expression databases can be visualised directly or compared to external samples provided by the user.

  由此产生的集成基因表达数据库可以直接可视化，也可以与用户提供的外部样本进行比较。

• See Tables 1–3 for an overview of the data presented in BloodSpot and the normalisation procedure used.

  见表1-3所示的血斑数据和使用的正常化程序的概述。

• All AML data sets available in BloodSpot are normalised according to Rapin et al. (10) and further batch corrected using ComBat when necessary.

  根据Rapin等人(10)的说法，血斑中所有可用的AML数据集都被归一化，并在必要时使用战斗修正进一步的批次。

• This processing schema ensures that the samples are normalised in the context of normal haematopoiesis and according to state of the art batch correction methods, regardless of the origin of the data.

  这种处理模式确保样本在正常造血的背景下，并根据最新的批量校正方法进行标准化，而不考虑数据的来源。

• For RNA-seq data, we used the Blue Collar Bioinformatics RNA-seq pipeline (mapping on mm10 mouse genome with TopHat version 2 (15), (https://bcbio-nextgen.readthedocs.org/)) to obtain normalised count data from raw fastq files from Lara-Astiaso et al. (16).

  对于RNA-seq数据，我们使用蓝领生物信息学RNA-seq管道(用TopHat version 2(15)在mm10小鼠基因组上进行映射，(https://bcbio-nextgen.readthedocs.org/))从Lara-Astiaso等人的原始fastq文件中获得标准化计数数据(16)。

• We report count data processed using the variance stabilising transformation method from the DESeq2 package (17).

  我们报告了从DESeq2包中使用方差稳定转换方法处理的计数数据(17)。

Abbreviations and sample annotations

• Abbreviations for all cell types can be found below the plot by clicking the ‘Abbreviations’ link.

  通过单击“缩写”链接，可以在图下方找到所有单元格类型的缩写。

• Typically, the user can find more detailed information about each cell type such as a longer, more informative name, and for healthy cells data sets the immunophenotype, when available.

  通常，用户可以找到关于每种细胞类型的更详细的信息，比如更长的、更有信息的名称，以及健康细胞数据集的免疫表型(如果有的话)。

• Links to the raw unprocessed data can also be found here.

  到原始未处理数据的链接也可以在这里找到。

Available genes

• The server is restricted to genes found in our database of Affymetrix Human 133U plus 2, Affymetrix Human 133UA and Affymetrix Human 133UB chips for human, and GeneChip Mouse Genome 430 2.0 and Affymetrix Mouse Gene 1.0 ST Arrays for mouse.

  服务器仅限于我们数据库中发现的Affymetrix Human 133U plus 2、Affymetrix Human 133UA和Affymetrix Human 133UB人类芯片、GeneChip小鼠基因组430 2.0和Affymetrix小鼠基因1.0 ST组小鼠基因。

• For the RNA-seq data set UCSC annotation for the mm10 genome was used.

  对于RNA-seq数据集，使用了mm10基因组的UCSC注释。

• In order to handle gene aliases, a dictionary of gene aliases was constructed from NCBI ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/ and The HUGO Gene Nomencla- ture Committee (HGNC)www.genenames.org.

  为了处理基因别名，从NCBI ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/和HUGO基因命名委员会(HGNC)www.genenames.org构建了一个基因别名字典。

• Ambiguous gene aliases were not included when constructing the dictionary.

  在构建字典时没有包含模糊的基因别名。

• The alias conversion is only used when the query is not an official gene symbol or probe name.

  别名转换仅在查询不是正式的基因符号或探针名称时使用。

• The end result allows for greater flexibility regarding gene names input and faster browsing.

  最终的结果允许在基因名称输入方面有更大的灵活性和更快的浏览速度。

FUNCTIONALITY UPDATES

• Both the backend and the front-end have been completely redesigned for interactive usage and speed ofexecution.

  后端和前端都已完全重新设计，以适应交互使用和执行速度。

• The interface is built with a range of new functionalities, with a focus on simplicity of use (see Figure 2).

  该接口使用一系列新功能构建，重点是使用的简单性(参见图2)。

  
  fig2

Unified input

• BloodSpot takes a single gene name (or unambiguous gene alias) or gene signature name as query.

  BloodSpot以单个基因名(或明确的基因别名)或基因签名名作为查询。

• Users can search for keywords such as ‘carcinomas’ or ‘cell cycle’ and will be provided with a list of matching gene signature names.

  用户可以搜索“癌”或“细胞周期”等关键词，并将获得匹配的基因签名名称列表。

• When relevant, it is possible to select which probe-set to display from the list in the upper right corner of the main plot.

  当相关时，可以从主图右上角的列表中选择要显示的probe-set。

• By default, the probe with the overall highest intensity is at the top of the list.

  默认情况下，总体强度最高的探测位于列表的顶部。

• The option ‘Max probe’ will use the one probe with the highest intensity within each population.

  选项“Max probe”将使用每个种群中强度最高的一个探针。

Default plot

• When visiting the interface the plot at the centre of the screen in the default view.

  当访问界面时，默认视图中屏幕中心的绘图。

• This representation is a novel improved jitter strip chart of gene expression, a swift novel visualisation plot that draws from bar plots and violin plots where the jitter is controlled by the density of samples and normalised over all the columns in the chart.

  这是一种新的改良的抖动基因表达条带图，一种快速的可视化图，它从条形图和小提琴图中提取，其中抖动由样本的密度控制，并在图中的所有列上进行标准化。

• Thus the width of the data cloud shows how many samples have similar values (see Figure 3A and a comparison to existing data plot types in Supplementary Figure S1).

  因此，数据云的宽度显示了有多少样例具有相似的值(参见图3A和补充图S1中与现有数据图类型的比较)。

  
  fig3A

• For more details on this visualisation method please see (Sidiropoulos, N.,Sohi, S.H., Rapin, N. and Bagger, F.O. (2015) SinaPlot: an enhanced chart for simple and truthful representation of single observations over multiple classes.bioRxiv, http://dx.doi.org/10.1101/028191).

  有关这种可视化方法的更多细节，请参见(Sidiropoulos, N.Sohi,萨达姆政权,Rapin, N.和Bagger, F.O. (2015) SinaPlot:一个简单而真实地表示多个类的单个观察的增强图表。bioRxiv,http://dxdoi.org/10.1101/028191)。

• Both an R-package and a web- server have been implemented for those interested in make use of this plot type that we have named SinaPlot.

  R-package和web- server都已经为那些有兴趣使用我们命名为SinaPlot的plot类型的用户实现了。

Survival plot

• The chart shown to the left of the BloodSpot interface is a survival plot based on a high-quality AML data set from The Cancer Genome Atlas (TCGA).

  血斑界面左侧的图表是基于癌症基因组图谱(TCGA)的高质量AML数据集绘制的生存图。

• It displays a full Kaplan–Meier analysis of survival.

  它展示了一个完整的Kaplan-Meier生存分析。

• The survival plots are only available for human data sets, sharing probes with the microarray platform used by the TCGA (Affymetrix U133 Plus 2) (see Figure 3B)

  生存图只适用于人类数据集，与TCGA (Affymetrix U133 + 2)使用的微阵列平台共享探针(见图3B)

  
  fig3B

Tree plot

• The chart shown to the right of the BloodSpot interface is an interactive hierarchical tree that shows the relationship between the samples displayed and allows changing the focus of the display.

  BloodSpot界面右侧显示的图表是一个交互式层次树，它显示显示的样本之间的关系，并允许更改显示的焦点。

• It is possible to mouse over the nodes to get the full name for long names.

  可以将鼠标移到节点上以获得长名称的全名。

• Nodes can be clicked to collapse a branch of the tree––this will also update the default plot in the middle and remove the same populations there (see Figure 3C).

  可以单击节点来折叠树的一个分支——这也将更新中间的默认图，并删除相同的填充(参见图3C)。

  
  fig3C

Correlation of genes and gene signatures

• For each gene and signature in every data set, we report the top correlating genes or signatures.

  对于每个数据集中的每个基因和签名，我们报告了顶部相关的基因或签名。

• Taking the haematopoietic fingerprint (e.g. the expression value of one gene over all haematopoietic cells) of all probe-sets and gene signatures in a given data set, we calculated the correlation matrix (Pearson) and present the highest positive and negative correlating genes/signatures.

  取给定数据集中所有探针组和基因签名的造血指纹图谱(例如一个基因在所有造血细胞上的表达值)，计算相关矩阵(Pearson)，并给出最高的正相关和负相关基因/签名。

• This feature allows for investigation of new associations between putative co-regulated genes or signatures that exhibit similar or inverse expression patterns over the course ofhaematopoiesis (see Figure 3D).

  这一特性允许研究在造血过程中表现出相似或相反表达模式的假定共同调控基因或特征之间的新关联(见图3D)。

  
  fig3D

Other built-in tools

• Cell populations may be removed from the graphs using the ‘Select population’ button.

  可以使用“选择种群”按钮从图中删除细胞种群。

• The current plot displayed can be exported as PDF in publication-ready quality using the ‘Print as PDF’ button.

  现时显示的图则可使用“列印为PDF”按钮，以PDF格式输出，以备出版。

• The ‘T-Test’ button can be used to add the results from a students t-test for significance be- tween pairs of populations to the plot.

  “T-Test”按钮可用于将学生T-Test的结果添加到图中，以确定两组总体之间的显著性。

• The legend is as fol- lowing: NS: non-significant;P < 0.05; * * P < 0.01;**P < 0.001。

  图例如下:NS:无显著性;P < 0.05; * * P < 0.01;**P < 0.001。

• The significance marks relies on t statistics for un- equal sample sizes but assuming equal variance and the critical values are compared with a two-tailed probability.

  显著性标记依赖于样本大小不相等时的t统计量，但假设方差和临界值相等，并与双尾概率进行比较。

• Finally, raw data can be exported as CSV using the ‘Export Data as Text’ button.

  最后，原始数据可以导出为CSV使用' Export data as Text '按钮。

Upload sample

• The analysis is anonymous and requires no login.

  分析是匿名的，不需要登录。

• The resulting data set, including the uploaded sample, can then be queried along with the default data sets in a private session.

  然后，可以在私有会话中查询结果数据集(包括上载的示例)和默认数据集。

• All names and array information are stripped from the uploaded file before creating the database for the user session.

  在为用户会话创建数据库之前，将从上传的文件中删除所有名称和数组信息。

• Hence, the uploaded sample in the private session will appear simply as S 1 in all charts.

  因此，私有会话中上传的示例将在所有图表中显示为s1。

• The private sessions and uploaded data are deleted every day at GMT 1.30 pm.

  每天下午1点30分，私人会议和上传的数据都会被删除。

EXAMPLES OF USE OF BLOODSPOT

• To demonstrate the use ofBloodSpot, we provide in the fol- lowing section an example relying on data and analysis pro- vided by the database.

  为了演示血斑的使用，我们在下一节提供了一个基于数据库提供的数据和分析的例子。

• MEIS1 is part of a transcriptional program required for the maintenance of MLL-rearranged AML (18).

  MEIS1是维持ml -重排AML(18)所需的转录程序的一部分。

• The ex- pression ofthis gene is therefore often up-regulated inMLL leukaemias.

  因此，该基因的表达在mll白血病中经常上调。

• Using Bloodspot, we investigated the expression pattern of MEIS1, and found it to be expressed at high levels in stem cells with decreasing expression as the cells differentiate (Figure 3A and C). Using the correlation function, we find that MEIS1 expression also correlates with the expression patterns of a number of Homeobox genes, including HOXA3, HOXA9 and HOXA10 which are also typically expressed early during haematopoiesis (19)(Fig- ure 3D).

  使用Bloodspot,我们调查MEIS1的表达模式,并发现它被表达在干细胞表达高水平与在细胞分化表达减少(图3 a和C)。使用相关函数,我们发现MEIS1表达式也与许多同源框基因的表达模式,包括HOXA3 HOXA9和HOXA10通常也表示在早期造血作用(19)(图-保证3 d)。

1559285224164.png

• Switching to the BloodPool data set, MEIS1 is found to be up-regulated in MLL leukaemias (Figure 4). Although the P-value in the survival plot does not reach statistical significance (0.055;

  切换到血池数据集，我们发现MEIS1在MLL白血病中上调(图4)，虽然生存图中的p值没有达到统计学意义(0.055;

• see Figure 3B), the influence of MEIS1 expression in leukemic patients may be of potential relevance

  见图3B)， MEIS1在白血病患者中的表达可能具有潜在的相关性

DISCUSSION

• Here we have presented a web-based database that al- lows for browsing of haematopoietic gene-expression fingerprints in human, murine and malignant haematopoiesis in a large number of high-quality data set containing several hematopoietic cell types.

  在此，我们提出了一个基于web的数据库，它可以在包含多种造血细胞类型的大量高质量数据集中浏览人类、小鼠和恶性造血中造血基因表达指纹图谱。

• The tool facilitates the easy assess- ment of gene-expression data and how this links to patient survival, investigation ofgene-expression signatures, as well as analysis of user generated data and export of data and figures.

  该工具易于评估基因表达数据以及如何将其与患者生存、基因表达特征的调查、用户生成数据的分析以及数据和图形的导出联系起来。

• Focusing on simplicity, BloodSpot has features that allow clinicians or biologists to quickly retrieve relevant in- formation on the expression ofspecific genes/pathways, and further explore co-regulated patterns of gene-expression as well as impact on patient survival.

  以简单性为重点，BloodSpot的特点使临床医生或生物学家能够快速检索特定基因/通路表达的相关信息，并进一步探索基因表达的协同调控模式以及对患者生存的影响。

• Our statistical frame- work supports the upload ofuser-generated patient data for integration and comparison with our database of healthy cells.

  我们的统计框架工作支持上传用户生成的病人数据，以便与我们的健康细胞数据库进行集成和比较。

• This will allow assessment of the origin of the blast population in AML patients as well as assessment of well known and novel genetic markers in the context of normal haematopoiesis, both ofwhich could be important for stratification of difficult patient cases.

  这将有助于评估AML患者中blast群体的起源，以及在正常造血过程中评估已知的和新的遗传标记，这两种方法对困难病例的分层可能都很重要。

• We have also integrated the largest pool ofAML patient microarray samples to date and have computed gene ex- pression fold changes for these profiles, thanks to our cancer versus normal method previously described in (10)and curation and labelling of external data followed by ComBat (12).

  我们还整合了迄今为止最大的aml患者微阵列样本池，并计算了这些基因表达谱的基因表达倍数变化，这得益于我们之前(10)中描述的癌症与正常方法的对比，以及外部数据的筛选和标记，以及随后的 ComBat(12)。

• In conclusion, we have curated and populated a database and developed an analysis platform, which will allow researchers as well as clinicians to access and analyse gene expression data related to both normal and malignant haematopoiesis.

  综上所述，我们已经策划并填充了一个数据库，并开发了一个分析平台，该平台将允许研究人员和临床医生访问和分析与正常和恶性造血相关的基因表达数据。

• We believe that the database should be of interest to all researchers and clinicians interested in haematopoiesis, leukaemia, basic immunology and gene expression in developmental systems.

  我们相信这个数据库应该引起所有对造血、白血病、基础免疫学和发育系统中的基因表达感兴趣的研究人员和临床医生的兴趣。

• Additional to information on gene-expression BloodSpot addresses two key questions, namely, how gene-expression patterns of single genes impact on patient survival, and which other genes display similar expression patterns in the haematopoietic system.

  除了关于基因表达血斑的信息外，还解决了两个关键问题，即单个基因的基因表达模式如何影响患者的生存，以及哪些其他基因在造血系统中表现出类似的表达模式。

• Thus the platform will help broaden the basis on which to generate hypotheses about potential therapeutic targets and expand the understanding of co-regulated genes and pathways, to support experimen- tal findings from animal model systems

  因此，该平台将有助于拓宽基础，在此基础上产生关于潜在治疗目标的假设，并扩大对共同调控基因和通路的理解，以支持来自动物模型系统的实验结果。

AVAILABILITY

Bloodspot is accessible at www.bloodspot.eu

SUPPLEMENTARY

Supplementary Data are available at NAR Online.

ACKNOWLEDGEMENTS

This work was supported by a grant from the Danish Re- search Council for Strategic Research, as well as through a centre grant from the NovoNordisk Foundation (The Novo Nordisk Foundation section for Stem Cell biology in Hu- man Disease). Furthermore, F.O.B. was supported by the Lundbeck foundation. We thank Nicolas Hillau for the an- imated logo of BloodSpot

FUNDING

REFERENCES