Skip to main content

Advertisement

TACITuS: transcriptomic data collector, integrator, and selector on big data platform

Article metrics

Abstract

Background

Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis.

Results

TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis.

Conclusions

TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

Background

Transcriptome analysis can be applied to define biomarkers in precision medicine, to describe how observed alterations impact a patient’s phenotype, or, more fundamentally, to infer causality among genes. To date, studies have produced a huge amount of data stored in databases such as NCBI GEO or ArrayExpress.

ArrayExpress [1] is a public database storing high-throughput functional genomics data, such as Microarray and Next-Generation Sequencing (NGS). Each dataset is provided by a user, either by submitting it directly, or by importing it from other databases (i.e. Gene Expression Omnibus, GEO). Directly submitted datasets are manually curated according to the MIAME (Minimum information about a Microarray experiment) standard [2] for Microarray, and MINSEQE (Minimum Information about a high-throughput nucleotide SEQuencing Experiment) standard for NGS.

These information standards support the sharing and reuse of scientific data. The MIAME standard was introduced in 2001 to simplify storing and exchanging gene expression experiments. The specifications of this standard requires the recording of all the information needed to unambiguously interpret the results of an experiment, and be able to reproduce the experiment. The standard defines the content and structure of necessary information, rather than the technical format for archiving. Similarly, MINSEQE defines a standard to allow the unambiguous interpretation and reproducibility of sequencing experiments.

Users who submit data to ArrayExpress provide their files together with metadata, describing sample content. Data are exported and stored according to the MAGE-TAB format [3]. Data sets imported from other functional genomics databases are also converted into MAGE-TAB files for archiving.

MAGE-TAB is a tabular MIAME file format. MAGE-TAB documents consist of five different types of files: (i) A ’raw’ archive; (ii) A ’data matrix’ file; (iii) The Sample and Data Relationship Format (SDRF) tab-delimited file; (iv) The Array Design Format (ADF) tab-delimited file; (v) The Investigation Description Format (IDF) tab-delimited file.

Retrieving publicly available data from ArrayExpress for analysis is a time-consuming task. First, some datasets are quite large so take time to download. Second, few tools are available to interact with ArrayExpress, though some are very good: in [4] authors presented the ArrayExpress package for R/Bioconductor to query ArrayExpress and convert MAGE-TAB formatted datasets from the ArrayExpress repository into objects of the Bioconductor class (eSet).

NCBI GEO is a public functional genomics data repository [5, 6] supporting MIAME-compliant data extraction. The system’s tools support user query and download experiments and curated gene expression profiles. End users may encounter problems when downloading large datasets.

To our knowledge, currently no system allows users to select small amounts of data from big experimental files. Consequently, users spend a lot of time to prepare data for further analysis. In addition, uploading data into Galaxy is complicated by the limited set of plugins offered by the platform and the need to manually pre-process external data.

Our system TACITuS (Transcriptomic Data Collector, Integrator, and Selector) is a web app that simplifies the process of collection, pre-processing, selection, and integration of transcriptomics data. Through our interface a user can collect data from major sources, such as NCBI GEO or ArrayExpress, and integrate them with their own data into a standardized format, facilitating subsequent analyses. Our tool, built on top of MongoDB, Apache Lucene, and proper indexing algorithms, can easily manage large amounts of data guaranteeing fast performance. In its current version, TACITuS supports only pre-processed NGS and Microarray dataset. Raw datasets can not be imported. Furthermore, all metadata are collected and standardized, enabling fast search and easy management on large datasets. Our software is freely available at https://tacitus.app/ and distributed through a GPL v3 licence. Finally, a module enabling complex data analysis through a connection with the Galaxy [7] computational platform is available.

To evaluate our methods, we imported several high dimensional transcriptomics datasets to determine efficiency in storage, pre-processing, and indexing. On such datasets, we also performed several selection queries and gathered their results to demonstrate the user experience. These experiments show that the user can import large datasets in a few hours, select samples in less than an hour, map and integrate datasets in a few minutes. All this is done with low memory and CPU consumption, making the software capable to scale and therefore suitable when large number of users are connected.

Implementation

TACITuS is built on top of the Laravel framework and is backed by two databases: MariaDB for storing fast indexes of all available datasets, and MongoDB for data and meta-data storing. Data processing is done in R, C++ and PHP to achieve high performances. Our portal collects data from the major data sources, NCBI GEO and ArrayExpress, and integrates them with user data. TACITuS offers five major functionalities: (i) data import, (ii) data selection, (iii) identifier mapping, (iv) data integration, and (v) Galaxy Export. In this section, we will provide a description of these modules and their implementation. For more details, an online step-by-step tutorial is available through the web interface.

Import

TACITuS enables users to import dataset from several public transcriptomics resources. Through the “Dataset submission” panel (see Fig. 1a), a user selects one data source (NCBI GEO, ArrayExpress or custom), specifies the accession number of the dataset to be imported (see Fig. 1b), and decides whether imported datasets should be public or private. In the current interface, NGS and Microarray datasets are supported if processed data are available. Raw datasets can not be imported in this release.

Fig. 1
figure1

Data import panel. a The user selects the “Submit Dataset” button in the “Datasets” panel. b By filling the “Dataset submission” form an import request can be submitted

When the request is submitted, a record is added into a priority queue with all user-specified parameters and the computation starts as soon as resources are available.

First, dataset files are downloaded. Next, all relevant data are stored in a MongoDB databases, while building two indexes to speed-up further phases: (i) a meta-data index, and (ii) a sample index. The meta-data index is a list of all sample descriptor attributes. The sample index is a map that associates samples to their positions in the expression matrix. Samples are identified by a unique code. Finally, for each meta-data attribute, a Lucene-backed full-text index is built and a record describing the dataset is stored in the database and made available to the user.

For NCBI GEO datasets, the platform descriptor, containing information on mappings between probes identifiers and the respective ones in other datasets, such as Entrez or Ensembl Gene Ids, is also downloaded and imported in the database.

Selection

TACITuS allows users to select portions of imported datasets through a meta-data search. Selection improves performance when working with large datasets that can not be entirely loaded into memory. By clicking on the “Make Selection” button in the list of datasets (see Fig. 1a), the “Sample Selection” panel will appear, allowing to the user to specify all parameters. Specifically, the user can provide a selection name, for easy content identification, and select the samples to include. The table lists all samples and their meta-data to simplify the selection process. Sample search by meta-data is supported by the full-text indices built during the import phase (see Fig. 2).

Fig. 2
figure2

Data selection panel. The “Sample selection” panel can be employed to produce a sub-datasets containing only portion of the samples. The selection table provides functions to filter samples by metadata attributes

After submitting the selection, a job is added to a priority queue, and processed when computational resources become available. As the process starts, selected sample meta-data are stored in a TSV (Tab-Separated Value) file and positions of such samples in the dataset expression matrix are extracted from the sample index. Next, all probes containing each sample set are gathered and stored in TSV files. Each line of the file contains the name of a probe, and the selected expression values. The first line of such a file contains the name of the selected samples.

As soon as the selection process is completed, the user can download both data and metadata in TSV or CSV format. If CSV format is chosen, the delimiter character can be specified.

Mapping

For each selection, TACITuS supports a mapping from probe identifiers to standardized, e.g., Entrez, Identifiers. This facilitates the data integration process when using different platforms, because standardized identifiers between different experiments can be easily matched.

The functionality is activated through the “Selections” panel, where the “Map Identifiers” button is located (see Fig. 3a). By making such a selection, a panel will open up. The desired platform for the transcriptomics experiment and the destination identifier are then selected (see Fig. 3b). If, as in NCBI GEO, the platform is automatically detected, the user can proceed directly to the destination identifier choice.

Fig. 3
figure3

Identifiers mapping panel. a The user selects the “Map Identifiers” button in the “Selections” panel. b By choosing a platform and a destination identifier in the “Map Identifiers” form a request is submitted

Once the request has been completed, a job is added to the priority queue. As soon as processing starts, a mapping table is built, where each probe identifier is associated with a standard one. The data file is read, and each probe identifier is replaced with the selected one, according to the mapping table. If no entry is available for an identifier, the probe will be removed from the output. The results are stored in TSV format. The first line contains the selected sample names, while the remaining lines contain the expression of probes for which a mapping is available. The user can choose to download data and metadata in TSV or CSV format.

Integration

TACITuS implements an integration procedure devised to combine two or more selections into a single dataset. The system exploits the following techniques: (i) Sims et al., 2008 [8], (ii) COMBAT [9], (iii) Gene Standardization, and (iv) Cross-platform Normalization (XPN) [10].

Sims et al., 2008 applies a technique like z-score normalization, transforming each dataset through mean-centering. COMBAT exploits an Empirical Bayesian model to estimate the mean and variance of each gene in each dataset, correcting data for batch effects. Gene Standardization is the simplest mathematical transformation to make datasets comparable. For each gene, the expression value is corrected subtracting the mean and dividing by the standard deviation. XPN finds blocks of genes and samples showing similar expression patterns and, therefore, uses the average of these blocks to shift and scale data.

The integration procedure starts by clicking on the “Request Integration” button available through the “Integrator” panel (see Fig. 4a). The user specifies which datasets to integrate, the combination method (see Fig. 4b). Integration algorithms can also be disabled if the user needs to combine expression matrices without altering the values.

Fig. 4
figure4

Integration panel. a The user requests the integrator procedure through the button in the “Integrator” panel. b The request can be submitted after choosing the dataset selection and the algorithm

Once the request has been completed, a job is added to the priority queue. The process will start as soon as the resources are available. First, all meta-data are merged in a single table, using the meta-data index to speed-up the identification of variables in the final table. All attributes from the original datasets are saved. If one or more samples in the merged table have the same identifier, a unique number will be appended. Next, all expression matrices are loaded in memory and the chosen algorithm builds a combined expression matrix. Finally, results are stored in TSV format and can be downloaded either in TSV or CSV format.

The integration procedure loads full expression matrices, therefore is a memory-intensive process.

Galaxy integration

TACITuS implements a module to upload data and metadata to the Galaxy platform. The integration with the Galaxy cloud environment has been done with Blend4php API [11]. To enable this module, the user has to provide the details of a galaxy server (name, host name, port) together with user’s credentials (API key) through the “Galaxy Account” panel in the “User Profile” page (see Fig. 5a). This activates the “Upload to Galaxy” button (see Fig. 5b). By clicking on the upload button beside each dataset, the user will open the uploading panel, enabling the selection of a Galaxy server for the upload (see Fig. 5c).

Fig. 5
figure5

Galaxy Upload Module. a The module is activated by adding at least one server in the “Galaxy Accounts” panel. b The user will be then able to use the “Upload to galaxy” button provided in all panels. c) Finally, the upload process can be initiated in the “Upload to Galaxy” panel

Once the selection has been made, the request is submitted to the priority queue and the process will start as soon as the requested resources become available. First, a connection to the remote Galaxy Server is created. Then an authentication request through the user API key is performed. If the request is successful, a new history with the dataset name is created. An history is a container in Galaxy maintaining the location of a dataset and the results of its analysis. Finally, data and metadata files are sent to the galaxy server for storage in the generated container.

Results

To show how our approach can manage large expression data, we perform a case study with the ArrayExpress dataset “A comprehensive human expression map” (E-MTAB-3732). This dataset has been selected because of its size and complexity (~ 30GB). The dataset consists of 27,887 samples comprising expression data from approximately 50,000 genes. All experiments were performed using the Affymetrix HG-U133Plus2 platform. For each sample descriptive variables identifying cell type, source tissue, cell line, and any pharmacological treatments are available. We imported the dataset, and performed several procedures evaluating the employed amount of RAM, percentage of CPU, and time. For all tests we used an Intel Xeon E5-2440 (6 cores - 12 threads) with 32Gb of RAM.

Furthermore, we evaluated the effect that integration methods have on expression datasets to understand which technique yields better results. For this purpose, two analysis were carried out on (i) synthetic datasets and (ii) real datasets taken from The Cancer Genome Atlas (TCGA).

First, two synthetic datasets were generated to simulate Microarray and NGS experiments, respectively. The Microarray dataset was generated using the R package madsim [12], while the compcodeR [13] package was used for the NGS one. For both datasets, recommended parameters were used, generating 7 case samples and 7 controls. Both packages, in addition to expression matrices, provide the list of true Differentially Expressed Genes (DEGs) and their expected direction (upregulation or downregulation). Then, each dataset was randomly partitioned into two subset of samples and the integration methods were applied on them. Finally, DEGs were estimated using Limma, varying the p-value threshold from 0.001 to 0.05. Next, Receiver-Operating Characteristic (ROC) curves were built by comparing estimated DEGs with true ones. Curves were summarized by computing the Area Under the ROC Curve (AUC). Furthermore, the same test was performed on the original matrix without any integration procedure to obtain a baseline AUC, since some DEGs might not be detected due to Limma.

For the simulated Microarray dataset (Fig. 6b), all methods obtain an AUC comparable to the baseline except for gene standardization which obtained  0.5. Therefore, we excluded the latter from Fig. 6b to allow a better visualization. For the simulated NGS dataset (Fig. 6a), the baseline method obtained a mean AUC greater than 0.65. Only COMBAT obtained a comparable value. The other three methods had significantly lower values.

Fig. 6
figure6

Effect of the integration procedures on synthetic expression datasets. The two datasets simulate Microarray data (a) and NGS data (b). Each synthetic dataset was randomly partitioned in two sets. Then, each integration procedure was applied and DEGs were computed varying the p-value threshold. Receiver-Operating Characteristic (ROC) curves were computed using true DEGs, and the performances were assessed by means of Area Under the ROC Curve (AUC)

The second test was performed on the Lung Squamous Cell Carcinoma (LUSC) dataset obtained from TCGA. The dataset comprises of 552 RNA-seq samples (501 cases and 51 controls) obtained using the Illumina HiSeq-2000 sequencing platform. Furthermore, expressions measured using Affymetrix HG-U133A were available for 131 of the 501 cases. First we built a reference set by determining DEGs on all RNA-seq samples using Limma (p<0.01). Next, we removed the RNA-seq samples corresponding to the 131 microarray cases. This yielded two different dataset. Then, the two series of samples were processed using integration algorithms. Finally, the performances were evaluated using the above described methodology. Results show that both Sims et al., 2008 and COMBAT are capable to obtain results more accurate than the baseline approach (Fig. 7).

Fig. 7
figure7

Effect of the integration procedures on TCGA LUSC datasets. Two datasets comprising of Illumina HiSeq-2000 and Affymetrix HG-U133A data were employed to evaluate the effect of the integration on two different platforms. After applying the integration procedure, DEGs were computed varying the p-value threshold. Receiver-Operating Characteristic (ROC) curves were computed using known DEGs, and the performances were assessed by means of Area Under the ROC Curve (AUC)

Discussion

TACITuS is a user-friendly system providing a web interface designed to simplify the process of collection, pre-processing, selection, and integration of large transcriptomics datasets. The system also enables uploading of data to Galaxy Servers to allow further analysis.

To show how our approach can easily manage large expression data, we perform a case study with the ArrayExpress dataset “A comprehensive human expression map” (E-MTAB-3732). The dataset has been imported through the “Submit Dataset” button in the “Datasets” panel. The system checked that all necessary parameters were specified. Next, the job was submitted to our analysis queue (see Fig. 8). In two hours, the system was able to import the dataset with minimal resource usage (~ 600MB of RAM and 8% sustained usage of a 6 cores CPU). At the end of the procedure, a notification was sent to the user, and the selection panel was enabled for the dataset (see Fig. 8).

Fig. 8
figure8

A common workflow in TACITuS. First, the user selects a dataset from a public database. As soon as the dataset is imported, selections on the samples can be performed. Finally, selection from multiple datasets can be integrated to obtain a single set of samples and their metadata

We decided to select samples from two cell lines: H125 (lung cancer samples) and H460 (non-small cell lung cancer samples). These have been separately submitted to TACITuS, to obtain two different sub-datasets. The system was able to generate such subsets in one hour consuming only 370MB of RAM. Once the data become available, we used the integration tool to build a single dataset and map probe identifiers to Entrez Gene IDs (see Fig. 8). The procedure took only a few minutes using a modest amount of memory, approximately 1GB of RAM. Finally, we uploaded the data in the Galaxy Public Server, and performed a differential expression analysis using Limma (see Fig. 9).

Fig. 9
figure9

Galaxy Analysis Pipeline. A description of the galaxy analysis pipeline used for our case study. First data were uploaded in Galaxy using TACITuS. Next, we applied Limma to determine if any differentially expressed genes were present

Our tests show that TACITuS can handle large datasets with modest computational resources. Moreover, the simplified interface allows inexperienced users to manage large datasets. To evaluate integration algorithms impact, we performed several experiments on synthetic and real datasets. Experiments carried out on synthetic datasets showed how COMBAT is the only integration method capable to yield results comparable both on Microarray and NGS data. However, the chosen technique seems to have less impact in NGS rather than Microarrays. These results are also confirmed in the TCGA LUSC dataset. Both COMBAT and Sims et al., 2008 were able to obtain superior results compared to the baseline approach.

Conclusions

TACITuS is a web app allowing users to manage and integrate data coming from ArrayExpress and NCBI GEO. Large transcriptomics datasets can be easily pre-processed, selected, and integrated. This enables many users to access large genome data usually available only to advanced bioinformaticians with large computational resources. The system allows also to upload data to the Galaxy platform for further analysis.

Availability and requirements

Project Name: TACITuS

Project Home page: https://tacitus.app/

Bugs Reporting: https://github.com/alaimos/tacitus/issues

Operating system: Platform independent

Programming languages: PHP, R, C++

Other requirements: All modern web browser

License: GNU GPL 3

Other restrictions: For non-academics, licence is needed

Availability of data and materials

The datasets analysed during the current study are available in the ArrayExpress repository (E-MTAB-3732), https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3732/.

Abbreviations

ADF:

Array design format

API:

Application programming interface

AUC:

Area under the ROC curve

DEG:

Differentially-expressed gene

GEO:

Gene expression omnibus

GPL:

GNU general public license

IDF:

Investigation description format

LUSC:

Lung squamous cell carcinoma

MAGE-TAB:

MicroArray gene expression tabular

MIAME:

Minimum information about a microarray experiment

MINSEQE:

Minimum information about a high-throughput nucleotide SEQuencing experiment

NCBI:

National center for biotechnology information

NGS:

Next-generation sequencing

ROC:

Receiver-operating characteristic curve

SDRF:

Sample and data relationship format

TACITuS:

Transcriptomic data collector, integrator, and selector

TCGA:

The cancer genome atlas

XPN:

Cross-platform normalization

References

  1. 1

    Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, et al. Arrayexpress update—from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res. 2009; 37(suppl 1):868–72.

  2. 2

    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, et al. Minimum information about a microarray experiment (miame)—toward standards for microarray data. Nat Genet. 2001; 29(4):365–71.

  3. 3

    Rayner TF, Rocca-Serra P, Spellman PT, Causton HC, Farne A, Holloway E, Irizarry RA, Liu J, Maier DS, Miller M, et al. A simple spreadsheet-based, miame-supportive format for microarray data: Mage-tab. BMC Bioinformatics. 2006; 7(1):1.

  4. 4

    Kauffmann A, Rayner TF, Parkinson H, Kapushesky M, Lukk M, Brazma A, Huber W. Importing arrayexpress datasets into r/bioconductor. Bioinformatics. 2009; 25(16):2092–4.

  5. 5

    Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. Ncbi geo: archive for functional genomics data sets—update. Nucleic Acids Res. 2012; 41(D1):991–5.

  6. 6

    Edgar R, Domrachev M, Lash AE. Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Res. 2002; 30(1):207–10.

  7. 7

    Afgan E, Baker D, Batut B, Van Den Beek M, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Grüning BA, et al. The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018; 46(W1):537–44.

  8. 8

    Sims AH, Smethurst GJ, Hey Y, Okoniewski MJ, Pepper SD, Howell A, Miller CJ, Clarke RB. The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets–improving meta-analysis and prediction of prognosis. BMC Med Genom. 2008; 1(1):42.

  9. 9

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics. 2007; 8(1):118–27.

  10. 10

    Shabalin AA, Tjelmeland H, Fan C, Perou CM, Nobel AB. Merging two gene-expression studies via cross-platform normalization. Bioinformatics. 2008; 24(9):1154–60.

  11. 11

    Wytko C, Soto B, Ficklin SP. blend4php: a php api for galaxy. Database. 2017;2017(1).

  12. 12

    Dembélé D. A flexible microarray data simulation model. Microarrays. 2013; 2(2):115–30.

  13. 13

    Soneson C. compcoder—an r package for benchmarking differential expression methods for rna-seq data. Bioinformatics. 2014; 30(17):2517–8.

Download references

Acknowledgements

The results presented in this manuscript are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.

About this supplement

This article has been published as part of BMC Bioinformatics, Volume 20 Supplement 9, 2019: Italian Society of Bioinformatics (BITS): Annual Meeting 2018. The full contents of the supplement are available at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-20-supplement-9.

Funding

SA, AF and AP have been partially supported by the research project "Marcatori molecolari e clinico-strumentali precoci, nelle patologie metaboliche e cronico-degenerative" founded by the Department of Clinical and Experimental Medicine of University of Catania. AP has been also partially supported by the italian MIUR FFABR grant. DS has been partially supported by the U.S. National Science Foundation under grants MCB-1412232, IOS-1339362, MCB-1355462, MCB-1158273, IOS-0922738, and MCB-0929339. This support is greatly appreciated. Publication costs are funded by the research project “Marcatori molecolari e clinico-strumentali precoci, nelle patologie metaboliche e cronico-degenerative” founded by the Department of Clinical and Experimental Medicine of University of Catania. Funding bodies did not play any role in the design of the study and collection, analysis, and interpretation of data.

Author information

SA, DS, and AP conceived the work. AF, DS, and AP coordinated the research. AS, ADM, and AP wrote the paper. AS and ADM developed the system. All authors analyzed the data, and reviewed the final version of the manuscript.

Correspondence to Salvatore Alaimo.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alaimo, S., Di Maria, A., Shasha, D. et al. TACITuS: transcriptomic data collector, integrator, and selector on big data platform. BMC Bioinformatics 20, 366 (2019) doi:10.1186/s12859-019-2912-4

Download citation

Keywords

  • RNA-Seq
  • Cloud storage and management
  • Galaxy