TRANSIT - a Software Tool for TnSeq Analysis

Welcome! This is the main website for the TRANSIT2 and TPP tools developed by the Ioerger lab at Texas A&M.

This page is for the new version of Transit, called Transit2. It can be installed as the transit2 package on PyPi using pip. The new version has a better, more integrated GUI. There are some minor differences in command-line arguments and file formats, but Transit2 still has the same methods for statistical analysis of TnSeq data as the original version of Transit.

The original version of Transit is still being maintained and distributed. It can be installed as the tnseq-transit package (most recently, v3.2.7) on PyPi using pip.

TRANSIT is a tool for the analysis of Tn-Seq data. It provides a easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality within a single condition as well as between conditions.
TPP is an optional pre-processing tool that allows the user to map reads to the genome of an organism, to create .wig files (with insertion counts at TA sites) that can be used in TRANSIT.
DeJesus, M.A., Ambadipudi, C., Baker, R., Sassetti, C., and Ioerger, T.R. (2015). TRANSIT - a Software Tool for Himar1 TnSeq Analysis. PLOS Computational Biology, 11(10):e1004401.
TRANSIT2 Online Documentation
Transit manual (PDF) (description of analytical methods and command-line operations for original version of Transit)
TRANSIT2 source code on GitHub
transit2 package on PyPi (install with pip3 - see documentation)
Genomes and 'prot_table' files commonly used with Transit
Pathway annotation files that can be used for 'pathway_enrichment' analysis (e.g. of resampling results)

Releases

## Version 1.1.5 (2024-10-16)

Minor changes:
 - ensure that font file is included in PyPi package


## Version 1.1.4 (2024-10-14)

Minor changes:
 - fixed a bug in TrackView that was caused by deprecated function in recent version of Python Image Library (PIL v10.0)


## Version 1.1.3 (2024-09-14)

Minor changes:
 - allow wig file pathnames to have spaces in combined_wig and metadata files (e.g. for ANOVA and ZINB)
 - changed default alg for BWA from 'mem' back to 'aln' (see documentation on TPP)


## Version 1.1.2, 2024-04-16
  - minor bug fix in 'cgi visualize'

Minor changes:
## Version 1.1.1, 2024-04-12

Minor changes:
  - updates to CGI functions (CRISPRi-DR) and documentation
  - 'cgi extract_counts' can now process *.fastq.gz files (automatically unzips them)
  - minor changes to how sgRNA ids are handled
  - minor updates to format of input files like sgRNA_info.txt
  - minor changes to command-line args and flags

## Version 1.1.0, 2024-04-12

Minor changes:
  - added empirical Bayes FDR analysis to filter significant interacting genes in CGI
  - added '-no_uninduced' flag to CGI command (see documentation)

## Version 1.0.14, 2024-04-04

Minor changes:
  - fixed a bug that caused GUI to hang after popup windows were closed

## Version 1.0.13, 2024-04-04

Minor changes:
  - fixed a bug for making combined_wig files in GUI using gff files

## Version 1.0.12, 2024-03-17

Minor changes:
  - fix minor bug in CRISPRi-DR (cgi) output

## Version 1.0.11, 2024-03-17

Minor changes:
  - minor updates to CRISPRi-DR (cgi) method and documentation


## Version 1.0.10, 2024-02-17
#### TRANSIT2:

Minor changes:
  - fixed .tolist() bug in betageom normalization in 'export combined_wig'


## Version 1.0.9, 2024-01-31

Minor changes:
  - a few updates to CRISRPi-DR (CGI)


## Version 1.0.8, 2023-12-20

Major changes:
  - added CRISPRi-DR method for analyzing CGI data (Chemical-Genetic Interactions)
    - includes GUI interface
    - see documentation
  - added confidence scores to HMM output

Minor changes:
  - fixed LOESS plots to show genome positional bias before and after correction


## Version 1.0.7, 2023-10-19

Minor changes:
  - bug fix in GUI for resampling


## Version 1.0.6 2023-10-18

Minor changes:
  - bug fix for corrplot (remove dependence on rpy2)
  - minor edits to documentation


## Version 1.0.5 2023-10-15

Minor changes:
  - fix something in .readthedocs.taml


## Version 1.0.4 2023-10-15

Minor changes:
  - fix docs on readthedocs by adding config file

	
## Version 1.0.3 2023-10-13

Minor changes:
  - Minor bugfix in ttnfitness

	
## Version 1.0.2 2023-07-15

Minor fix:
  - ensured that all sub-directories are included in distribution

	
## Version 1.0.1 2023-07-11

Updated documentation:
  - clarify that this is Transit2
  - put a link to the original version of Transit
  - update installation instructions for pip and git



## Version 1.0.0 2023-05-31

Major new release.
  - Re-implmentation from scratch.
  - more integrated GUI
  - some command-line arguments and file formats have changed from the original version of Transit
  - everything revolves around combined_wig files and metadata files, now, which facilitates analysis of larger TnSeq datasets with multiple conditions

Source Code

TRANSIT can be downloaded from the public GitHub repository, http://github.com/ioerger/transit2. It is released under a GPL License. You can download a zip file with the latest code or use git to clone the repository:


git clone https://github.com/ioerger/transit2/

Instructions

Make sure all prequisites are installed (see Documentation). Extract files into a single directory and in a terminal type:

python <PATH>/src/transit.py

Note: this command installs the source files locally in your directory, so you can run it from the command-line as above. Alternatively, you can use 'pip install' (see Documentation) to install a copy of transit in a global location on your system, like /usr/local/bin/transit. (transit becomes a command; you don't have to say 'python' before it) For detailed instructions, please see the documentation included with the source-code distribution or visit the following page:

TRANSIT2 Documentation

Prerequisites

TRANSIT2 requires python3.

In order for TRANSIT and TPP to run, various packages installed, like BWA, Numpy, Scipy, wxPython, matplotlib, R, etc.
See the documentation for full requirements and installation instructions.

Genome Annotation Files

Transit uses a custom file format for genome annotations called a 'prot_table', which is just a tab-separated file with a line for each ORF and specific columns of info. Examples of prot tables are available below.

If the annotation of your genome is in .gff (or .gff3) format, there is a command available in Transit to convert them to .prot_table format for use by all the analytical methods. Conversion of .gff files can also be done through the GUI (as a menu option).

> python transit.py convert gff2prot_table <.gff> <.prot_table>

Example Data

Example datasets (.wig) format at provided in the data/ directory included in the distribution. The accompanying genome (.fna) and annotation file (.prot_table) are included in the genomes/ directory. Additional genomes to be used with TRANSIT and TPP are available on the following site:

http://saclab.tamu.edu/essentiality/transit/genomes/

References

If you use TRANSIT2, please cite this paper:

DeJesus, M.A., Ambadipudi, C., Baker, R., Sassetti, C., and Ioerger, T.R. (2015). TRANSIT - a Software Tool for Himar1 TnSeq Analysis. PLOS Computational Biology, 11(10):e1004401.

Papers on the Statistical Methods in Transit:

Ioerger, T.R. (2022). Analysis of Gene Essentiality from TnSeq Data Using Transit. in: Zhang R. (eds) Essential Genes and Genomes. Methods in Molecular Biology, vol 2377. Humana, New York, NY, 2377:391-421. pubmed

Choudhery, S., Brown, A.J., Akusobi, C., Rubin, E.J., Sassetti, C.M., and Ioerger, T.R (2021). Modeling site-specific nucleotide biases affecting Himar1 transposon insertion frequencies in TnSeq datasets. mSystems, 6(5):e0087621. pubmed

Subramaniyam, S., DeJesus, M.A., Zaveri, A., Smith, C.M., Baker, R.E., Ehrt, S., Schnappinger, D., Sassetti, C.M., and Ioerger, T.R. (2019). Statistical analysis of variability in TnSeq data across conditions using Zero-Inflated Negative Binomial Regression. BMC Bioinformatics, 20(1):603. pubmed

DeJesus, M.A., Nambi, S., Smith, C.M., Baker, R.E., Sassetti, C.M., and Ioerger, T.R. (2017). Statistical Analysis of Genetic Interactions in TnSeq Data. Nucleic Acids Research, 45(11):e93. pubmed

DeJesus, M.A. and Ioerger, T.R. (2016). Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes. Journal of Bioinformatics and Computational Biology, 14(3):1642004. pubmed

DeJesus, M.A., Ambadipudi, C., Baker, R., Sassetti, C., and Ioerger, T.R. (2015). TRANSIT - a Software Tool for Himar1 TnSeq Analysis. PLOS Computational Biology, 11(10):e1004401. pubmed

DeJesus, M.A. and Ioerger, T.R. (2015). Reducing type I errors in Tn-Seq experiments by correcting the skew in read count distributions. 7th International Conference on Bioinformatics and Computational Biology (BICoB 2015). PDF.

DeJesus, M.A. and Ioerger, T.R. (2014). Capturing uncertainty by modeling local transposon insertion frequencies improves discrimination of essential genes. IEEE Transactions on Computational Biology and Bioinformatics, 12(1):92-102. pubmed

DeJesus, M.A. and Ioerger, T.R. (2013). A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data. BMC Bioinformatics, 14:303. pubmed

DeJesus, M.A. and Ioerger, T.R. (2013). Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data. ACM Conference on Bioinformatics, Computational Biology, and Biomedical Informatics (ACM-BCB), Washington, DC, Sept 22-25, 2013. pdf

DeJesus, M.A., Zhang, Y.J., Sassettti, C.M., Rubin, E.J., Sacchettini, J.C., and Ioerger, T.R. (2013). Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries. Bioinformatics, 29(6):695-703. pubmed

Biology papers from our group using TnSeq and Transit:

Zhang, L., Hendrickson, R.C., Meikle, V., Lefkowitz, E.J., Ioerger, T.R., and Niederweis, M. (2020). Comprehensive analysis of iron utilization by Mycobacterium tuberculosis. PLoS Pathogens, accepted.

Dragset, M., Ioerger, T.R., Loevenich, M., Haug, M., Sivakumar, N., Marstad, A., Cardona, P., Klinkenberg, G., Rubin, E.J., Steigedal, M., and Flo, T. (2019). Global assessment of Mycobacterium avium subspecies hominissuis genetic requirement for growth and virulence. mSystems, 4(6):e00402-19.

Dragset, M.S., Ioerger, T.R., Zhang, Y.J., Zekarias, Maerk, M., Ginbot, Z., Sacchettini, J.C., Flo, T.H., Rubin, E.J., Steigedal, M. (2019). Genome-wide phenotypic profiling identifies and categorizes genes required for mycobacterial low iron fitness. Scientific Reports, 9(1):11394. pubmed

Rego, H., Baranowski, C., Welch, M., Sham, L.-T., Eskandarian, H., Lim, H., Kieser, K., Wagner, J., McKinney, J., Fantner, G., Ioerger, T.R., Walker, S., Berhardt, T., and Rubin, E.J. (2018). Maturing Mycobacterium smegmatis peptidoglycan requires non-canonical crosslinks to maintain shape. eLife, e37516. pubmed

Carey, A.F., Rock, J.M., Krieger, I.V., Chase, M.R., Fernandez-Suarez, M., Gagneux, S., Sacchettini, J.C., Ioerger, T.R, and Fortune, S.M. (2018). TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities. PLOS Pathogens, 14(3):e1006939. pubmed

Xu, W., DeJesus, M.A., Rucker, N., Engelhart, C., Wright, M.G., Healy, C., Lin, K., Wang, R., Park, S.W., Ioerger, T.R., Schnappinger, D., and Ehrt, S. (2017). Chemical genomic interaction profiling reveals determinants of antibiotic susceptibility in Mycobacterium tuberculosis. Antimicrobial Agents and Chemotherapy, 61(12):e01334-17. pubmed

DeJesus, M.A., Gerrick, E.R., Xu, W., Park, S.W., Long, J.E., Boutte, C.C., Rubin, E.J., Schnappinger, D., Ehrt, S., Fortune, S.M., Sassetti, C.M., and Ioerger, T.R. (2017). Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis. mBio, 8(1):e02133-16. pubmed

Korte, J., Alber, M., Trujullo, C.M., Syson, K. Koliwer-Brandl, H., Deenen, R., Köhrer, K., DeJesus, M.A., Hartman, T., Jacobs, W.R. Jr., Bornemann, S., Ioerger, T.R., Ehrt, S., Kalscheuer, R. (2016). Trehalose-6-phosphate-mediated toxicity determines essentiality of OtsB2 in Mycobacterium tuberculosis in vitro and in mice. PLOS Pathogens, 12(12):e1006043. pubmed

Kieser, K.J., Baranowski, C., Chao, M.C., Long, J.E., Sassetti, C.M., Waldor, M.K., Sacchettini, J.C., Ioerger, T.R, and Rubin, E.J. (2015). Peptidoglycan synthesis in Mycobacterium tuberculosis is organized into networks with varying drug susceptibility. PNAS, 112(42):13087-92. pubmed

Zhang, Y.J., Reddy, M.C., Ioerger, T.R., Rothchild, A.C., Dartois, V., Schuster, B.M., Trauner, A., Wallis, D.E., Galaviz, S., Huttenhower, C., Saccettini, J.C., Behar, S.M., and Rubin, E.J. (2013). Tryptophan biosynthesis protects mycobacteria from CD4 T cell-mediated killing. Cell, 155(6):1296-308. pubmed

Zhang, Y.J., Ioerger, T.R., Huggenhower, C., Chen, X., Mohaideen, N., Long, J., Sassetti, C.M., Sacchettini, J.C. and Rubin, E.J. (2012). Global assessment of genomic regions required for growth in Mycobacterium tuberculosis. PLoS Pathogens, 8(9):e1002946. pubmed

Griffin, J.E., Gawronski, J.D., DeJesus, M.A., Ioerger, T.R., Akerley, B.J., Sassetti, C.M. (2011). High-resolution phenotypic profiling defines genes essential for mycobacterial survival and cholesterol catabolism. PLoS Pathogens, 7(9):e1002251. pubmed

Long, J.E., DeJesus, M., Ward, D., Baker, R.E., Ioerger, T.R. and Sassetti, C.M. (2015). Identifying essential genes in Mycobacterium tuberculosis by global phenotypic profiling. in: Methods in Molecular Biology: Gene Essentiality, (Long Jason Lu, ed.), vol. 1279.

Contact

Email questions to Tom Ioerger (ioerger@cs.tamu.edu).