The resampling method is a comparative analysis the allows that can be used to determine conditional essentiality of genes. It is based on a permutation test, and is capable of determining read-counts that are significantly different across conditions.

See Pathway Enrichment Analysis for post-processing the hits to determine if the hits are associated with a particular functional catogory of genes or known biological pathway.


Can be used for both Himar1 and Tn5 datasets

How does it work?

Briefly, the normalized read-counts at all TA sites are determined for each replicate of each of two conditions. For each gene, the mean read-count in condition A is subtracted from the mean read-count in condition B to obtain an observed difference in means. The counts are then permuted within the gene for a given number of times (“samples”). For each of these permutations, the difference in read-counts is determined. This forms a null distribution, from which a p-value is calculated for the original, observed difference in read-counts. (see Figure 3 in [DeJesus2015TRANSIT]).

Pooled vs Site-Restricted (S-R) Resampling

9/21/2022: The original version of resampling implemented in Transit is called ‘pooled’ resampling because the counts are permuted among all samples (replicates, conditions) and among all the TA sites in a gene (i.e. randomizing counts within and between TA sites). This was based on the assumption that insertions at any TA site are equally likely (and the only differences are due to stochastic differences in abundance in the transposon insertion library when constructed).

We recently developed an improved version of resampling called “site-restricted” (S-R) resampling. With S-R resampling, during the process of permuting the counts to generate the null distribution, the counts at each TA site are restricted to permutations among samples within the same TA site (the counts at each site are permuted independently). This restriction has the effect of reducing the variance in the null distribution, because there is evidence that there is an insertion bias for the Himar1 transposon that causes some TA sites to have a higher propensity for insertions and hence higher insertion counts than others (Choudhery et al, 2021).

Testing on a wide range of TnSeq datasets suggests that S-R resampling is more sensitive than regular pooled resampling. Typically, S-R resampling finds 2-3 times as many significant conditionally essential genes, mostly including the ones found by pooled resampling, but also capturing more borderline cases that were close to the previous significance cutoff.

It is important to note that the previous pooled version of resampling that has been in Transit for many years is not wrong, just overly conservative.

S-R resampling is incorporated in the v3.2.7 version of Transit. There is an option (i.e. checkbox) for it in the GUI interface for the resampling method, and there is a new command-line flag (-sr) if you want use it in console mode.


> python3 resampling <comma-separated .wig control files> <comma-separated .wig experimental files> <annotation .prot_table or GFF3> <output file> [Optional Arguments]
> python3 resampling -c <combined wig file> <samples_metadata file> <ctrl condition name> <exp condition name> <annotation .prot_table> <output file> [Optional Arguments]

NB: The ctrl and exp condition names should match Condition names in samples_metadata file.

      Optional Arguments:
      -s <integer>    :=  Number of samples. Default: -s 10000
      -n <string>     :=  Normalization method. Default: -n TTR
      -h              :=  Output histogram of the permutations for each gene. Default: Turned Off.
      -a              :=  Perform adaptive resampling. Default: Turned Off.
      -ez             :=  Exclude rows with zero accross conditions. Default: Turned off
                          (i.e. include rows with zeros).
      -PC <float>     :=  Pseudocounts used in calculating LFC. (default: 1)
      -l              :=  Perform LOESS Correction; Helps remove possible genomic position bias.
                          Default: Turned Off.
      -iN <float>     :=  Ignore TAs occuring at given percentage (as integer) of the N terminus. Default: -iN 0
      -iC <float>     :=  Ignore TAs occuring at given percentage (as integer) of the C terminus. Default: -iC 0
      --ctrl_lib      :=  String of letters representing library of control files in order
                          e.g. 'AABB'. Default empty. Letters used must also be used in --exp_lib
                          If non-empty, resampling will limit permutations to within-libraries.

      --exp_lib       :=  String of letters representing library of experimental files in order
                          e.g. 'ABAB'. Default empty. Letters used must also be used in --ctrl_lib
                          If non-empty, resampling will limit permutations to within-libraries.
      -winz           :=  winsorize insertion counts for each gene in each condition
                          (replace max count in each gene with 2nd highest; helps mitigate effect of outliers)
      -sr             :=  site-restricted resampling; more sensitive, might find a few more significant conditionally essential genes


The resampling method is non-parametric, and therefore does not require any parameters governing the distributions or the model. The following parameters are available for the method:

  • Samples: The number of samples (permutations) to perform for each gene (to generate the null distribution of mean differences in counts, for calculating p-values). The larger the number of samples, the more resolution the p-values calculated will have, at the expense of longer computation time. The resampling method runs on 10,000 samples by default.
  • Output Histograms: Determines whether to output .png images of the histograms obtained from resampling the difference in read-counts.
  • Adaptive Resampling: An optional “adaptive” version of resampling which accelerates the calculation by terminating early for genes which are likely not significant. This dramatically speeds up the computation at the cost of less accurate estimates for those genes that terminate early (i.e. deemed not significant). This option is OFF by default. (see Notes below)
  • Include Zeros: Select to include sites that are zero. This is the preferred behavior, however, unselecting this (thus ignoring sites that) are zero accross all dataset (i.e. completely empty), is useful for decreasing running time (specially for large datasets like Tn5).
  • Normalization Method: Determines which normalization method to use when comparing datasets. Proper normalization is important as it ensures that other sources of variability are not mistakenly treated as real differences. See the Normalization section for a description of normalization method available in TRANSIT.
  • –ctrl_lib, –exp_lib: These are for doing resampling with datasets from multiple libraries, see below.
  • -iN, -iC: Trimming of TA sites near N- and C-terminus. The default for trimming TA sites in the termini of ORFs is 0. However, TA sites in the stop codon (e.g. TAG) are automatically excluded. Trimming is specified as a percentage (as an integer), so, for example, if you want to trim TA sites within 5% of the termini, you would add the flags ‘-iN 5 -iC 5’ (not 0.05).
  • –Pval_col <int>: index of column with P-values (starting from 0)
  • -PC: Pseudocounts used in calculation of LFCs (log-fold-changes, see Output and Diagnostics) in resampling output file. To suppress the appearance of artifacts due to high-magnitude of LFCs from genes with low insertion counts (which are more susceptible to noise), one can increase the pseudocounts using `-PC’. Increasing PC to a value like 5 (which is reasonable, given that TTR normalization scales data so average insertion counts is around 100) can further reduce the appearance of artifacts (genes with low counts but large LFCs). However, changing pseudocounts only affects the LFCs, and will not change the number of significant genes.
  • -winz: winsorize insertion counts for each gene in each condition. Replace max count in each gene with 2nd highest. This can help mitigate effect of outliers.
  • -sr: use ‘site-restricted’ resampling (see description above)


I recommend using -a (adaptive resampling). It runs much faster, and the p-values will be very close to a full resamping run (doing all 10,000 samples for each gene).

Occasionally, people ask if resampling can be done on intergenic regions as well. It could be done pretty easily (for example by making a prot_table with coordinates for the regions between genes). But it is usually not worthwhile, because most intergenic regions are small (<100 bp) contain very few TA sites (often 0-2), making it difficult to make confident calls on essentiality.

Doing resampling with a combined_wig file

Resampling can also now take a combined_wig_ file as input (containing insertion counts for multiple sample), along with a samples_metadata_ file that describes the samples. This mode is indicated with a ‘-c’ flag. If you want to compare more than two conditions, see ZINB.


python3 resampling -c <combined_wig> <samples_metadata> <control_condition_name> <experimental_condition_name> <annotation .prot_table or GFF3> <output file> [Optional Arguments]


python3 resampling -c antibiotic_combined_wig.txt antibiotic_samples_metadata.txt Untreated Isoniazid H37Rv.prot_table results.txt -a

Doing resampling with datasets from different libraries.

In most cases, comparisons are done among samples (replicates) from the same library evaluated in two different conditions. But if the samples themselves come from different libraries, then this could introduce extra variability, the way resampling is normally done. To compensate for this, if you specify which libraries each dataset comes from, the permutations will be restricted to permuting counts only among samples within each library. Statistical significance is still determined from all the data in the end (by comparing the obversed difference of means between the two conditions to a null distribution). Of course, this method makes most sense when you have at least 1 replicate from each library in each condition.

Doing resampling between different strains.

The most common case is that resampling is done among replicates all from the same Tn library, and hence all the datasets (fastq files) are mapped to the same refence genome. Occasionally, it is useful to compare TnSeq datasets between two different strains, such as a reference strain and a clinical isolate from a different lineage. Suppose for simplicity that you want to compare one replicate from strain A (e.g. H37Rv) and one replicate from strain B (e.g. CDC1551). Resampling was not originally designed to handle this case. The problem is that the TA sites in the .wig files with insertion counts might have different coordinates (because of shifts due to indels between the genomes). Furthermore, a given gene might not even have the same number of TA sites in the two strains (due to SNPs). A simplistic solution is to just map both datasets to the same genome sequence (say H37Rv, for example). Then a resampling comparison could be run as usual, because the TA sites would all be on the same coordinate system. This is not ideal, however, because some reads of strain B might not map properly to genome A due to SNPs or indels between the genomes. In fact, in more divergent organisms with higher genetic diversity, this can cause entire regions to look artificially essential, because reads fail to map in genes with a large number of SNPs, resulting in the apparent absence of transposon insertions.

A better approach is to map each library to the custom genome sequence of its own strain (using TPP). It turns out the resampling can still be applied (since it is fundamentally a test on the difference of the mean insertion count in each gene). The key to making this work, aside from mapping each library to its own genome sequence, is that you need an annotation (prot_table) for the second strain that has been “adapted” from the first strain. This is because, to do a comparison between conditions for a gene, Transit needs to be able to determine which TA sites fall in that gene for each strain. This can be achieved by producing a “modified” prot_table, where the START and END coordinates of each ORF in strain B have been adjusted according to an alignment between genome A and genome B. You can use this web app: Prot_table Adjustment Tool, to create a modifed prot_table, given the prot_table for one strain and the fasta files for both genomes (which will be aligned). In other words, the app allows you to create ‘B.prot_table’ from ‘A.prot_table’ (and ‘A.fna’ and ‘B.fna’).

Once you have created B.prot_table, all you need to do is provide both prot_tables to resampling (either through the GUI, or on the command-line), as a comma-separated list. For example:

> python3 resampling Rv_1_H37Rv.wig,Rv_2_H37Rv.wig 632_1_632WGS.wig,632_2_632WGS.wig H37Rv.prot_table,632WGS.prot_table resampling_output.txt -a

In this example, 2 replicates from H37Rv (which had been mapped to H37Rv.fna by TPP) were compared to 2 replicates from strain 632 (which had been mapped to 632WGS.fna, the custom genome seq for strain 632). The important point is that two annotations are given in the 3rd arg on the command-line: H37Rv.prot_table,632WGS.prot_table. The assumption is that the ORF boundaries for H37Rv will be used to find TA sites in Rv_1_H37Rv.wig and Rv_2_H37Rv.wig, and the ORF boundaries in 632WGS.prot_table (which had been adapted from H37Rv.prot_table using the web app above) will be used to find TA sites in the corrsponding regions in 632_1_632WGS.wig and 632_2_632WGS.wig.

Note that, in contrast to handling datasets from different libraries disucssed above, in this case, the assumption is that all replicates in condition A will be from one library (and one strain), and all replicates in condition B will be from another library (another strain).

Output and Diagnostics

The resampling method outputs a tab-delimited file with results for each gene in the genome. P-values are adjusted for multiple comparisons using the Benjamini-Hochberg procedure (called “q-values” or “p-adj.”). A typical threshold for conditional essentiality on is q-value < 0.05.

Column Header Column Definition
Orf Gene ID.
Name Name of the gene.
Description Gene description.
Sites Number of TA sites in the gene.
Mean Ctrl Mean of read counts in condition 1. (avg over TA sites and reps)
Mean Exp Mean of read counts in condition 2.
log2FC Log-fold-change of exp (treatment) over ctrl (untreated)
Sum Ctrl Sum of read counts in condition 1.
Sum Exp Sum of read counts in condition 2.
Delta Mean Difference in the MEAN insertion counts.
p-value P-value calculated by the permutation test.
Adj. p-value Adjusted p-value controlling for the FDR (Benjamini-Hochberg)

log2FC: (log-fold-change, LFC) For each gene, the LFC is calculated as the log-base-2 of the ratio of mean insertion counts in the experimental (treated) condition vs. the control condition (untreated, reference). The default is PC=1, which avoids the result being undefined for genes with means of 0 in either condition. Pseudocounts can be changed using the -PC flag (above).

LFC = log2((mean_insertions_in_exp + PC)/(mean_insertions_in_ctrl + PC))


A typical run of the resampling method with 10,000 samples for each gene will take around 45 minutes (with the histogram option ON). Using the adaptive resampling option (-a), the run-time is reduced to around 10 minutes.