Andrew Lane, University of California, Berkeley
20150930 (v. 2.0): Major overhaul. The iPython notebook examples/implementation have been
deprecated (moved to the archive folder) and the core logic abstracted into the
20150530 (v. 1.0): Initial release.
eating package is a set of functions designed to help in planning, implementing and
analyzing CRISPR-EATING enzymatically generated sgRNA libraries for genome labeling
or screening as decribed in Lane et al., 2015
Given that CRISPR-EATING protocol produces sgRNAs from any source of DNA, it's essential to be able to predict, tune and analyze the sgRNA output of the protocol.
*Please contact email@example.com if you would like access to a multithreaded Amazon Web Services-deployable version. This version has modfications that separate database and parallelized scoring/BLAST threads, making it much faster and more scaleable when scoring large numbers of guides but somewhat more complex to deploy. SQLite databases with scores for all possible guides in hg19/GRCh37 are also available.
For each function, parameters are described in the function's docstring.
eating package addresses this as follows:
Prediction is covered by
- Given an input DNA sequence,
baseprovides functions to predict the output of CRISPR-EATING when the molecular biology protocol is applied to that sequence.
base.digest_targetsimulates PAM selection, cutting and MmeI-20mer trimming of input DNA to produce a list of sgRNAs
base.score_guidesuses the BioPython BLAST interface on a local BLAST installation and database to generate target-genome-wide off-target analysis of sgRNAs and processes the resulting hit data into a per-guide score by implementing the sgRNA off-target scoring algorithm described in Hsu et al., 2013.
- The implementation memoizes and stores the calculated scores in an sqlite database that can be re-used in other workflows
base.count_non_overlapping_guidesis a helper function to aggregate a linear cluster of guides and count the true non-overlapping count of guides within that region. This is useful for applications such as CRISPR-imaging, where guide tiling density is the desired characteristic.
- Given an input DNA sequence,
Tuning is covered by
designpcr.find_ampliconssearches for regions of an input genome that, when PCR amplified, will be digested into tens or hundreds of highly-specific sgRNAs. The resulting
Ampliconclass stores information about the location of the guides within the genome, the identities of the individual guides, their scores and, importantly, how close these clusters are to low-scoring guides that must be avoided in downstream PCR.
designpcr.primer_searchproposes and specificity-screens primers to amplify chosen amplicons
- Primers are proposed using the fast primer3-py interface
designpcr.screen_primer_in_silico_pcrimplements an in-silico PCR strategy to determine if primers proposed by primer3-py are likely to be successful on the target genome. Each primer is BLAST-ed against the template genome and the distance and orientation between matching sites in the genome is analyzed to ensure that off-target binding of primers cannot produce an amplicon. In practice, around 85% of the predicted-good primers output by this tool produce a single band in PCR using the protocol described the CRISPR-EATING paper.
designpcr.collect_good_primersoutputs primers and amplicon attributes, including size of amplicon and number of guides generated from an individual amplicon into CSV file for ordering of oligonucleotides from IDT.
- Sample Output:
Gene Sequence_id forward_seq forward_start forward_length forward_tm forward_gc reverse_seq reverse_start reverse_length reverse_tm reverse_gc input_seq_length PCR_product_length Guides_Contained Non-overlapping Guide Count GNB2L1 6282 GGGATAGGGACGGGGAGAAC 432 20 61.12018408 65 ACCCTCCGGAAGCACAGTT 1600 19 61.1473871 57.89473684 1596 1168 41 36 EF1A 3947 ACAGAAGCAACCAAAAATCAAACTT 157 25 58.82737049 32 TCCCTTCCAGGCGGCCTC 1948 18 63.80873271 72.22222222 1942 1791 43 36 EEF1G 13188 AATGCCACTCTCCAGGATGA 202 20 58.41176058 50 AGGAGGTGGGAGGGACAG 1984 18 59.54788591 66.66666667 1989 1782 59 52
Analysis is covered by a limited number of functions (
eating.visualize). These are designed to operate on FASTQ files resulting from HiSeq 2000 sequencing of CRISPR-EATING libraries.
visualize.plot_complexityattempts to describe the diversity of the resulting sgRNA library by plotting unique sequences against sequence count. This is intended to be a quick screen for gross PCR "jackpotting" (aka the overamplification of one or a few sequences in PCR).
visualize.string2featannotates Bio.Seq objects with the locations of sgRNAs observed in sequencing. This is useful for troubleshooting, e.g., failures of the Mung-Bean nuclease step where observed guides are preferentially found at the termini of amplicons.
eating folder from this git repo into to your python
site-packages directory or your project working directory.
Consult individual function docstrings for instructions or contact firstname.lastname@example.org for code orientation.