# RBseq
Track fitness of deletion mutants with a randomly barcoded random insertion library
Please note, this is scientific software for research purposes only. It is offered freely in the hope that it will be useful, but with no guarantees whatsoever. It has been tested, but only modestly. Proceed with caution.
Updated November 20, 2020
This software is currently unpublished, but is based on analysis published in 'Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides' by Coradetti et al. 2018 (DOI: 10.7554/eLife.32110.001) and Rapid Quantification of Mutant Fitness in Diverse Bacteria by Sequencing Randomly Bar-Coded Transposons by Wetmore et al 2015 (DOI 10.1128/mBio.00306-15).
--OVERVIEW--
RBseq is a python-based pipeline for performing genome-wide experiments to quantify the relative fitness of random insertion mutants with unique sequence barcodes. This software was developed specifically with fungal genomes in mind, but theoretically should work with any eukaryotic genome.
The RBseq package consists of four programs which each carry out a distinct step required for fitness analysis. The process is (1) map insertion locations and associate barcodes, (2) annotate the genomic context of those insertions, (3) count barcodes in different conditions, and then (4) calculate fitness scores for each gene, aggregating data from several barcoded insertions per gene. Once a given 'pool' of mutant strains has been characterized by (1) and (2), (3) and (4) can be repeated numerous times on diverse experiments starting with the same mutant pool. For a detailed example of this process see "Rapid Quantification of Mutant Fitness in Diverse Bacteria by Sequencing Randomly Bar-Coded Transposons" by Wetmore et al 2015 (DOI: 10.1128/mBio.00306-15) and "Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides" by Coradetti et al 2018 (DOI: 10.7554/eLife.32110).
RBseq_Map_Insertions.py
This program takes Illumina sequence reads as input and associates unique sequence barcodes with unique genomic insertion locations. Typically these are sequence reads from a TnSeq experiment or similar variation in which a known sequence in the inserted DNA is used to amplify the insertion/genome junction. While it is agnostic as to the method of insertion library construction, it assumes some rate of concatamer formation. That is, it does not assume inserted DNA is always incorporated into the genome in single copy at any given location. The type of insertion is tracked (concatameric vs single copy) and barcode that cannot be unambiguously mapped to single locus are filtered out. It also does not assume perfect editing of the insertion, some loss of sequence at the insertion/genome junction is tolerated. These features were included specifically for analysis of libraries constructed with Agrobacterium tumefaciens mediated transformation, but the software should be compatible with any random insertion library in which a defined length of random sequence barcodes are placed within a short sequence read of identifiable genomic sequence.
RBseq_Annotate_Insertions.py
This program uses a gff formatted file of gene locations to assign the mapped insertion locations as intergenic or as interrupting a protein coding gene. It also computes some summary information about the insertion pool for method development, such as the rate insertion in various genomic features, biases for GC content or across different scaffolds, etc.
RBseq_Count_BarCodes.py
This program takes as input Illumina sequence reads of barcode sequences (BarSeq) from a collection of experimental conditions. It computes some summary statistics on the observed barcode sequences and collects counts for barcodes mapped and annotated by RBseq_Map_Insertions.py and RBseq_Annotate_Insertions.py
RBseq_Calculate_Fitness.py
This program takes as input the barcode counts from RBseq_Count.py and calculates a fitness score for each gene [log2(condition abundance/reference abundance)] with a sufficient number of barcoded insertions with sufficient sequence depth, as well as statistical significance of those fitness scores in the form of T-like statistics (as described in Wetmore et al 2015, DOI: 10.1128/mBio.00306-15) P-values from those T-like statistics and a more conservative P-value from a Wilcoxon Signed Rank Test.
--PREREQUISITES--
-Python 3.7
-Biopython 1.72 or later
-Anaconda scientific computing environment or the following python libraries: numpy, pandas, matplotlib, json, scipy, and stats models
-NCBI BLAST+ 2.2.30 or later
Most recently tested with the following versions of python packages:
python: 3.7.6
biopython: 1.76
numpy: 1.18.5
pandas: 1.0.5
scipy: 1.5.0
matplotlib: 3.2.2
statsmodels: 0.11.1
--USAGE--
Each script requires a metadata file to list input sequencing files, required reference files and some common options, including output file locations. These text files are passed to the script with the -m or --metafile flag. The scripts provide a series of status updates to STDOUT and simultaneously save them in log file for easy reference later. By default, the log files are given a unique name based on the date, but you can change this with the optional -l or --logFile flag.
--
RBseq_Map_Insertions.py Inputs
python RBseq_Map_Insertions.py --metafile METAFILE [--logFile LOGFILE --qual 10 --matchBefore 6 --matchAfter 6 --matchJunction 6 --maxFillerSeq 10 --barcodeVariation 2 --wobbleAllowed 1 --scoreDiff 10 --minFrac 0.8 --filterNeighborhood 10 --filterEditDistance 5 --minPercentID 95 --maxEvalue 0.1 --noInsertHits --noBarcodes --useMappedFiles]
-m/--metafile METAFILE
Metadata file for TnSeq runs. A tab-delimited file with columns as follows:
Pool
A short name for the mutant pool being analyzed. Should represent a physical sample of thousands to millions of unique mutation events in one mixed culture. Multiple mutant pools can be analyzed with one metafile.
ShortName
A short, unique name for each fastq file.
Fastq
Full or relative path to fastq file, e.g sequenceFolder/experiment/run.fastq
ReadModel
Full or relative path to a text file describing the expected structure of TnSeq reads. The file consists of 4 lines. Line 1 denotes the number of initial variable bases to ignore as 'N's. Line 2 is the sequence preceding the barcode. Line 3 is the expected length of sequence barcode denoted as 'N's. Line 4 is the sequence between the barcode and the junction with the genome. Example:
nnnnnn
GATGTCCACGAGGTCTCT
NNNNNNNNNNNNNNNNNNNN
CGTACGCTGCAGGTCGACAATGATCCAAACTATCAGTGTTTGA
InsertionSequence
Full or relative path to a text file describing giving the full sequence of each insertion. This should be a fasta format file with two entries labeled 'insert' and 'plasmid' giving the full sequence of the expected insertion into the genome and and the remaining source plasmid sequence, if appropriate. Example:
>insert
agtcac...
>plasmid
actgact...
GenomeSequence
Full or relative path to the basename of a BLAST database for the relevant genome. This should be the same basename of the fasta source file. Example: 'path_to/dir/genome' should point to a folder 'dir' containing 'genome.fasta', 'genome.nhr', 'genome.nin', and 'genome.nsq'
OutputDir
Full or relative path to the desired directory to save output files.
GeneLocations
Full or relative path to a GFF file describing gene locations
GeneIdentifier
Name of unique gene identifiers from the GFF file to use as gene names. This should be one of the semicolon-delimited fields in mRNA entries in the GFF. If nothing is entered in this column 'Parent' will be used as default.
GeneAnnotations
Full or relative path to a tab-delimited file with columns labeled 'ID', 'AlternateID', and 'Annotation'. ID should be the unique gene identifier you are using as gene names and should match the field you specified in the metafile column 'GeneIdentifier'. AlternateID is an alternate, perhaps more convenient gene name. ID will likely be a cryptic systematic name such as 'RTO4_01234', but Alternate ID might be 'Arg9' or something more descriptive. 'Annotation' is a brief text describing the predicted gene function. This file allows you to set your preferred gene name and annotations for final output with fitness data later.
-l/--logFile LOGFILE
File to write run log to. Default is Map_TIMESTAMP.log
-q/--qual MINQUAL
Minimum quality score for the barcode region for a read to be mapped. Default is 10
-b/--matchBefore MATCHBEFORE
Number of bases in the constant region before the barcode to match. Default is 6
-a/--matchAfter MATCHAFTER
Number of bases in the constant region after the barcode to match. Default is 6.
-j/--matchJunction MATCHJUNCTION
Number of bases to match at the junction with the genome. Default is 5. Because under many library construction strategies there is often loss of sequence at the end of the inserted fragment, this script attempts to find a short segment of sequence matching the end of the insertion, scanning for the closest matching segment to the end of the sequence. Sequence after that furthest match is used as the junction with the native genome sequence.
-f/--maxFillerSeq MAXFILLERSEQ
NOT IMPLEMENTED YET. The maximum number of bases allowed between the detected end of the insertion and the start of a match to the genome. This could be used in future versions to report insertions with long pieces of filler DNA added at insertion sites.
-v/--barcodeVariation BARCODEVARIATION
Allowed deviation from expected barcode length. Default is 2. Depending on the method of library construction, significant numbers of barcoded insertions may have slightly shorter or longer random barcodes. Allowing flexibility in barcode length can recover these insertions, but slows down processing.
-w/--wobbleAllowed WOBBLEALLOWED
Allowed deviation from expected position in the read for the matching sequence around the barcode and the junction with the genome. Default is 1. Indels introduced near the barcode can leave some insertion unmapped without some allowance for small deviations from expected positions of search sequences.
-s/--scoreDiff SCOREDIFF
For a read mapping to the genome to be counted as unique, the best-hit bitscore must be at least this much greater than the second-best hit bitscore. Default is 10
-F/--minFrac MINFRACTION
Minimum fraction of reads mapping to a single location before barcode is classified as multilocus. Default is 0.9
-N/--filterNeighborhood FILTERNEIGHBORHOOD
Number of basepairs around each insertion to filter out less abundant insertions with similar barcodes. Default is 10
-D/--filterEditDistance FILTEREDITDISTANCE
Nearby barcodes within this Levenshtein edit distance will be filtered. Default is 5. This argument is ignored if --noBarcodes is passed (all insertions within --filterNeighborhood will be filtered)
-p/--minPercentID MINPERCENTID
Minimum percent identity required when BLASTing sequences to the genome or insert. Default is 95
-e/--maxEvalue MAXEVALUE
Maximum E-value allowed when BLASTing sequences to the genome or insert. Default is 0.1
-u/--useMappedFiles
Bypass the mapping step and work from previously mapped files in the output directory. Useful for testing the effects of some parameters without repeating time consuming mapping of reads.
-I/--noInsertHits
Skip search for additional insertion sequence after the expected junction with the genome. Default behavior is to look for these sequences, typically indicating concatameric insertions. Turning it off will save time if concatamers aren't common in your mutant pool.
-n/--noBarcodes
This flag is used for optimizing library construction protocols with non-barcoded sequences. As generating a diverse pool of insertion constructs with unique barcodes can be time consuming, a test insertion library can be created with constructs containing just one constant barcode. If TnSeq is performed on the resulting library, the distinct locations of insertions can be mapped, but they will all have the same sequence barcode associated with the insertion. To allow characterization of the library with this script, this flag signals to use a snippet of the associated genomic sequence as a substitute for a sequence barcode on the insertion. This allows for distinct insertions to be mapped and estimation of library diversity and identification of biases in insertion position, etc.
--
RBseq_Map_Insertions.py Outputs
POOL_poolfile
Tab-delimited file describing the pool of insertion mutants for which unique barcodes mapped to a single unambiguous genomic location. This file is used for downstream analysis. Other outputs are provided for troubleshooting analysis and library construction.
POOL_poolfile_ambiguous
Barcodes ambiguously mapping to two or more locations. These are barcodes inserted into sequence with one or more very similar sequences in the genome, or with poor quality reads for mapping the junction to the genome, creating uncertainty as to where exactly the barcode is inserted.
POOL_poolfile_filteredLocal
Barcodes that were mapped close to a very similar, more abundant barcode
POOL_poolfile_insertReadsOnly
Barcodes that only mapped to insert sequences. These could be barcodes in concatameric insertions for which the junction with the genome is difficult to amplify or in multi-barcode concatamers
POOL_poolfile_multiLocus
Barcodes with reads mapping to two or more distinct locations. Most likely the same sequence barcode inserted in two or more different genomic locations
POOL_poolfile_offByOne
Barcodes that differed by just one base from much more abundant barcodes. Almost certainly sequencing errors.
TNSEQ_FILE_blastGenome.txt
Results from the BLAST search of sequences from TNSEQ_FILE against the genome.
TNSEQ_FILE_blastInsert.txt
Results from the BLAST search of sequences from TNSEQ_FILE against the genome.
TNSEQ_FILE_blastquery.fasta
Sequences blasted agains the genome/insert from TNSEQ_FILE
TNSEQ_FILE_mapped.txt
Summary of each read blasted against the genome/insert, barcode sequence found and information about the blast results
TNSEQ_FILE_noBarcodeFound.txt
Reads for which the compliant barcode sequences were not found.
--
RBseq_Annotate_Insertions.py Inputs
python RBseq_Annotate_insertions.py -m metadatafile [--logFile logfile --promoterLength 500 --terminatorLength 100]
-m/--metafile METAFILE
Metadata file for TnSeq runs. Same as for RBseq_Map_Insertions.py. For each unique entry in the Pool column of the metafile, this script will look for files named POOL_poolfile in the matching 'OutputDir' column. Gene annotations are taken from the GFF file specified in the column 'GeneLocations'. This GFF should contain mRNA features and CDS features to identify transcript and exon boundaries. The Parent field in the mRNA features is the default source of gene ids to add to the pool file, but any field specified by Name=value in the GFF's mRNA features can be used by setting the 'GeneIdentifier' field in the metafile to Name. The 'GeneAnnotations' field is the path and name of tab-delimited text file containing custom annotations and alternate names for your genes. This file should contain the headers 'ID','AlternateID', and 'Annotation' where 'ID' contains the same identifiers as present in the GFF file.
-l/--logFile LOGFILE
File to write run log to. Default is Annotate_TIMESTAMP.log
-P/--promoterLength PROMOTERLENGTH
Number of bases before transcriptional start to count as promoter sequences. Default is 500
-T/--terminatorLength TERMINATORLENGTH
Number of bases after transcriptional stop counted as terminator sequence. Default is 100
--
RBseq_Annotate_Insertions.py Outputs
POOL_poolfile_annotated
The input poolfile with information on the nearest gene to each insertion and the type of genomic feature the insertion is in (promoter, exon, intergenic, etc)
POOL_poolfile_GChistogram.pdf
A histogram of GC content around insertion sites versus GC content in the genome. Included to detect GC bias in insertion rates.
POOL_poolfile_insertionDistribution.pdf
A graph comparing frequency of insertions in promoters, exons, etc versus percent of the genome those features comprise. Included to detect feature bias in insertion rates. With many insertional strategies, higher insertion rates are common in more open regions such as promoters. In some species or with some methods this bias may be extreme enough as to make generation of a large number of insertions in coding regions much more difficult.
POOL_poolfile_insertionDistribution.txt
A text file giving raw data for the insertion distribution graph
POOL_poolfile_InsertsPerScaffold.pdf
Graphs of insertion number versus scaffold length. Useful for detecting bias in insertion rates or problems with genome assembly or insertion mapping.
POOL_poolfile_LargestScaffold.pdf
Graph of insertion density in a 1000 base-pair rolling window across the largest scaffold, and predicted insertion density if insertions occurred randomly at the same relative rate in promoters, eons, etc as measured in this library. Useful for understanding how well distributed insertions occur. Empirically most insertion libraries are not quite random, that is less evenly distributed than a random model would predict, but useful libraries don't have any large scale biases in insertion rates or particularly dominant insertion hot-spots.
--
RBseq_Count_Barcodes.py Inputs
python RBseq_Count_Barcodes.py -m metadatafile [-l logfile --matchBefore 6 --matchAfter 6 --qual 10]
-m/--metafile METAFILE
Metadata file for BarSeq runs. Contains the following columns:
FileIndex
An unique integer for each fastq file to be processed. Used to store intermediate files listing counts for all complaint barcodes found in a given fastq file. Note these are counts for all possible barcodes seen in the sequence, not only those found in the mapped insertion pool reference file.
Fastq
Full or relative path to barseq fastq files.
SampleName
Short unique name for each physical sample. One physical sample can have multiple fastq files with their own parameters and dual indexes. All counts across the different fastq files will be aggregated for fitness analysis. So biological replicates should have different sample names such as treatment_a, treatment_b, treatment_c, control_a, etc. Whereas technical replicates should have the same sample names.
UsePrecounted
TRUE or FALSE. If TRUE then computed barcode counts from a previous run will be loaded from the output directory. Useful when re-running analysis on an updated pool mapping or annotation.
Poolfile
Full or relative path to annotated poolfile from RBseq_Annotate_Insertions.py
OutputDir
Full or relative path to output directory
minRandom
The minimum number of expected random bases in the barseq reads. Often barseq primers contain a number of leading random bases to assist in clustering on the illumina sequencer
maxRandom
The maximum number of expected random bases. In some implementations barseq primers are mixtures with different lengths of leading random bases. In these cases minRandom and maxRandom should cover the expected range of leading random bases, otherwise maxRandom should be equal to minRandom.
DualIndex
In some applications BarSeq primers will contain a second index sequence to guard against 'index hopping' on the illumine sequencer. In cases for which the dual indexes have not be de-multiplexed by the sequencer, this field can be used to specify the expected dual index sequence. This is assumed to appear in the BarSeq read after the leading random bases and before the sequence specified in 'BeforeBarcode'
BeforeBarcode
The constant sequence before the barcode sequence. Not all of this sequence need be matched to identify the barcode, on the range specified with the -b flag.
BarcodeLengths
The expected length of the barcode. Can be a single number e.g. 20, or a comma-separated list with no spaces of allowed lengths, e.g. 20,19,21 This list should be in order of expected abundance for fastest processing.
AfterBarcode
The constant sequence after the barcode sequence. Not all of this sequence need be matched to identify the barcode, on the range specified with the -a flag.
-l/--logFile LOGFILE
File to write run log to. Default is Count_TIMESTAMP.log
-q/--qual MINQUAL
Minimum quality score for the barcode region for a read to counted. Default is 10
-b/--matchBefore
Number of bases before the barcode to match. Default is 6
-a/--matchAfter
Number of bases to match after the barcode. Default is 6. Note that in some cases this search region may be approaching the end of 50bp BarSeq reads. If matchAfter happens to span the end of the read, and thus there aren't as many bases to match as set here, but there are 2 or more bases to mach, as many bases are available up to the number specified will be matched. If there aren't 2 or more bases to match, then the read will be discarded.
-Q/--quietMode
If this flag is passed the script will run in 'quiet mode' and give fewer details from each fastq on the command line and in the log file. All the same statistics will still be reported in fastqSummaryStats.txt
RBseq_Count_Barcodes.py Outputs
RBseq_Count_Barcodes.py will summarize the number of barcodes seen for each fastqfile, and use the most abundant barcode to estimate overall error rate (the percent of reads with sequence errors in the barcode). It reports the number barcodes seen once, twice or three times or more in the fastq file, as well as an an extremely approximate guess at to the real diversity of barcodes present in the physical sample using the formula postulated by (Chao 1987): N seen once squared divided by two times N seen twice. The Chao estimate can be useful when read depth is limiting and the vast majority of one or two count barcodes are real. At higher read depths the one and two count barcodes are more inflated with sequencing errors and the number of barcodes with three or more counts is probably a better indicator of the actual population size.
poolCount.txt
Tab-delimited file with uniquely mapped barcodes in the insertion pool, some information about their location and genomic context, and the number of times each was seen in each sample. Note that for a given biological sample multiple different fastq files can be loaded into this script representing different technical replicates, repeated sequence runs, etc, but in the final output, counts for each sample (indicated by the SampleName field in the metafile) will be aggregated across all input fastqs.
countsFiles
A directory of files with raw barcode counts seen in each fastq file. These include barcodes not in the mapped mutant pool. These files can be used to repeat counts with an updated poolfile without the time consuming step of finding and counting barcodes in all reads.
fastqSummaryStats.txt
A tab-delimited file summarizing some statistics on barcodes seen in each sequence file. These are the same stats reported on the command line (total reads, reads with barcodes, etc). Note that for any files with UsePrecounted = TRUE in the metafile some of these statistics won't be reported as any information on reads without complaint barcodes are not saved in the intermediate .counts files.
--
RBseq_Calculate_Fitness.py Inputs
python RBseq_Count_Barcodes.py -m metadatafile [--logFile logfile --normLocal 0 --fitnessStartBoundary 5 --fitnessEndBoundary 95 --minInsertionCounts 3 --minGeneCounts 20 --minInsertions 3 --maxWeightCounts 50 --noPseudoCounts --fitnessBrowserOutput]
-m/--metafile METAFILE
Metadata file for fitness calculation. Contains the following columns:
Condition
A succinct, but informative name for an experimental condition. This identical name should be repeated on each line for biological replicates from that condition.
Sample
Name for each biological sample for fitness calculation. Should match SampleName columns in the RBseq_Count_Barcodes.py metadata file.
Reference
The reference sample for each experimental sample. In the most basic fitness experiments this will be either a 'time 0' of recovered mutant pool before it is grown under the experimental condition, or a sample started from the same seed culture as the experimental sample and grown the same time or same number of cell divisions in a reference condition. The same reference sample can be used for multiple conditions, if appropriate, for example if three independent starter cultures were each used seed independent replicates for several different experimental conditions.
Paired
TRUE or FALSE. Ideally each for each condition of interest there will be three or more biological replicates, each of which was seeded from an independent starter culture used as a reference, or which can each be paired to a replicate in the reference condition which was seeded form the same starter culture. Paring of these samples is important for testing statistical significance of fitness scores. If FALSE is passed for any of the samples in a given condition, then normalized BarSeq counts for all passed referenced samples will be averaged and used a common reference sample for each replicate. This would be the case, for example, if a single starter culture was used to seed three biological replicates for a given condition and three technical replicate samples were taken from that one culture as reference samples.
OutputDir
Output directory for results. Only the first entry in this column is actually used.
PoolCountFile
Poolcounts file from RBseq_Count_Barcodes.py
-l/--logFile LOGFILE
File to write run log to. Default is Fitness_TIMESTAMP.log
-L/--normLocal NEIGHBORHOOD
Integer. By default normalization is achieved by normalizing barcode counts in the mapped pool across input samples. However in some species (particularly in bacteria) large biases in observed fitness scores are seen across the genome. If correcting these large scale biases is desirable, passing -L 200 for example will normalize fitness scores across a sliding window of 200 kilobases such that the median strain fitness is 0.
-s/--fitnessStartBoundary STARTBOUNDARY
Integer 0-100. Fraction of the region between coding start and coding stop to set as start boundary for calculating gene fitness. Default is 5: i.e. insertions in the first 5 percent of the region between start and stop will not be included in calculations for gene fitness scores or statistical tests
-e/--fitnessEndBoundary ENDBOUNDARY
Integer 0-100. Fraction of the region between coding start and coding stop to set as end boundary for calculating gene fitness. Default is 95: i.e. insertions in the last 5 percent of the region between start and stop will not be included in calculations for gene fitness scores or statistical tests
-i/--minInsertionCounts INSERTCOUNTS
Integer. Minimum counts each insertion must have between both the test condition and the reference condition to be included in calculations for gene fitness scores or statistical tests. Default is 3
-g/--minGeneCounts GENECOUNTS
Integer. Minimum total counts across all insertions in a given gene before gene fitness scores are calculated. Default is 20
-I/--minInsertions INSERTIONS
Integer. Minimum number of insertions with sufficient counts in a given gene before gene fitness scores are calculated. Default is 3
-W/--maxWeightCounts COUNTS
Integer. Maximum counts to be used for gene fitness score calculation. E.g. if set at 50, then two insertions with 50 and 100 counts would be weighted equally in computing gene fitness, but an insertion with 10 counts would have a smaller weight
-P/--noPseudoCounts
If this flag is passed, fitness scores will NOT be computed with 'smart' pseudo counts as in Wetmore et al 2015. 'Smart' pseudo counts improve the accuracy of fitness scores when one condition has very low or no counts. As smart pseudo counts use data from all insertions in a gene to adjust the counts used for computing fitness scores for individual insertions, it can be argued that they abolish independence of those scores and compromise the stringency of any statistical analysis of those scores. This author believes that they are, on balance, useful, but others may disagree.
-C/--centerOnMean
By default strain fitness scores are normalized to a median of zero in a given sample. If this flag is passed they will be normalized to a mean of zero.
-N/--minReadsMainLocation
Minimum number of reads used define the barcode's chromosomal location in the mapping step, passed through to the poolcount file as nMainLocation. Barcodes mapped with only a few reads may not be accurately located, or the mapped barcode might also exist in greater abundance in another strain. Default is 2.
-R/--maximumConcatamerRatio
The maximum allowable ratio of reads mapped to the insertion sequences versus reads mapped to the genome. If sequenceing efficiency were constant, this ratio plus one would be a measure of the length of concatameric insertions. If this ratio is high it could indicate the barcode incorporated into a long stretch of concatenated insertions sequences, or that it has inserted in several locatoins in the genome, but only one location where the junction with the genome was mapped. Default is 8.
-B/--fitnessBrowserOutput
If this flag is passed, a subdirectory in the output directory called Fitness Browser will be created with output files compatible with the LBNL fitness browser by Morgan Price (https://bitbucket.org/berkeleylab/feba). If this flag is passed, the metadata file will require these additional column headings:
Date -OR- Date_pool_expt_started
Date of experiment
SetName
setA, setB, setC... etc labels for each set of related experiments performed from the same starter cultures at the same time
Group
A short informative name grouping experiments. E.g. 'Carbon_sources' or 'Antibiotics', etc.
Index
A name for each condition (could simply be a repeat of the condition column, or something more compact like a short abbreviation)
short
A short human-readable name for each condition
Condition_1
A more complete description for each condition, or key variable.
RBseq_Calculate_Fitness.py Outputs
geneFit.txt
Fitness scores aggregated across all insertions in each gene, averaged between biological replicates for each condition
geneFit_individual_replicates.txt
Fitness scores aggregated across all insertions in each gene, but with scores for individual replicates conserved
Tstats.txt
T-like test statistics calculated as in Wetmore et al, 2015 with all strain fitness scores aggregated for each gene across biological replicates.
Tstats_individual_replicates.txt
T-like test statistics calculated as in Wetmore et al, 2015 with all strain fitness scores aggregated for each gene, but with Tstats calculated for each replicate
pVals.txt
P-values from T-like test statistics if treated as true T-statistics, after multiple hypothesis correction with the Benjamini-Hochberg procedure.
pVals_Wilcoxon.txt
P-values from a Wilcoxon Signed Rank Test on normalized BarSeq counts in each condition versus reference samples, after multiple hypothesis correction with the Benjamini-Hochberg procedure. The signed rank test is less sensitive than T-test approach, but assumes nothing about the distribution of the data, whereas the Pvalues from the Tstats are only valid when the Tstats approximate a normal distribution for truly random data (see QQplots below). Thus the signed rank test P-values should be considered conservative but more robust.
qualityStats.txt
A table of summary statistics that may inform the overall quality of the experiments
Condition
Name of condition
nMapped
Number of barcodes seen in this condition and reference samples
nGenic
Number of barcodes in the analyzed portion of gene coding regions in this condition and reference samples
nUsed
Number of barcodes actually used for fitness calculations
gMed
Median counts per gene in condition samples
gMedt0
Median counts per gene in reference samples
gMean
Mean counts per gene in conditions samples
cor12
Average correlation between fitness scores from insertions in the first and second half of the gene
mad12
Mean absolute difference in fitness scores from insertions in the first and second half of the gene
gccor
Correlation between strain fitness scores local GC content
adjcor
Correlation between fitness scores for adjacent genes
maxFit
Maximum fitness score observed in the experiment
specificPhenotypes.txt
A table of genes and conditions for which magnitude of fitness scores was greater than the 95 percentile across all experiments + 0.5. That is is a table of uniquely strong fitness scores.
strainFit.txt
A table of fitness scores calculated for every insertion tracked
strainFitUsed.txt
A table of fitness scores for only the insertions used in fitness analysis
QCplots
A folder with the following plots for each condition
CONDITION_cor12.pdf
A plot of fitness scores calculated only from insertions in the first half of the gene versus those calculated in the second half of the gene. In a usable experiment, data points should mostly cluster around the origin, with a line of significant fitness scores along a diagonal line through the origin
CONDITION_fit_v_position.pdf
A plot of gene fitness scores versus chromosomal position on the largest scaffold in the genome. In a usable experiment data points should be cluster around the zero axis without any clear positional bias. If positional biases are observed consider using the -L/--normLocal option.
CONDITION_GCcor.pdf
A plot of strain fitness versus local GC percent.
CONDITION_qq.pdf
A quantile-quantile plot of Tstats versus a standard normal distribution. If the Tstats followed a perfect standard normal, they would fall along the diagonal line on the plot. Tstats from theoretically identical populations (e.g. a collection of comparisons between 'Time 0' replicates, or Tstats from the mutant pool grown in the same condition as the library was constructed) should hold closely to this line. In an experiment in which the Tstats are useful, the Tstats should follow this diagonal line near the origin, then diverge at the edges of the distribution as genes with true fitness effects have more extreme T-stats than would be expected from a normal distribution. If Tstats do not follow this expected pattern, use of the more conservative P-values from the Wilcoxon Signed Rank Test is recommended over using P-values from the Tstats.
--LICENSE--
Copyright (C) 2019 Samuel Coradetti and the United States Department of Energy. All rights reserved.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
--DISCLAIMER--
NEITHER THE UNITED STATES NOR THE UNITED STATES DEPARTMENT OF ENERGY NOR THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, NOR ANY OF THEIR EMPLOYEES, MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF ANY INFORMATION, APPARATUS, PRODUCT, OR PROCESS DISCLOSED, OR REPRESENTS THAT ITS USE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS.