cell culture performed as previously described . briefly , k562 cells ( atcc ) were cultured in rpmi 1640 ( gibco ) media and supplemented with 10% fbs ( hyclone ) , penicillin ( 10,000 i.u./ml ) , streptomycin ( 10,000 ug / ml ) , and l - glutamine ( 2 mm )
. cells were grown in log phase during all biological assays by returning the population to 500,000 cells / ml each day .
k562 cells were maintained in a controlled humidified incubator at 37 c , with 5% co2 .
briefly , to generate sufficient lentivirus to infect the genome - wide shrna library into k562 cells , we plated 293 t cells on 15 cm tissue culture plates .
293 t cells were transfected with third generation packaging plasmids and shrna encoding vectors . after 48 hrs and 72 hrs of incubation
we filtered the pooled lentivirus through a 0.45 m pvdf filter ( millipore ) to remove any cellular debris .
approximately 560 million k562 cells were infected with our next - generation genome - wide lentiviral shrna library to maintain roughly 1,000-fold coverage of the shrna library after selection .
infected cells grew for 3 days before selecting the cells with puromycin ( 0.7 g / ml , sigma ) .
after three days of selection , infection efficiency was monitored using flow cytometry ( bd accuri c6 ) .
once the cells reached 90100% mcherry positive cells , they were spun out of selection and allowed to recover in normal rpmi 1640 media . at t0
, 500 million cells were pelleted by centrifugation ( 300 g for 5 min ) .
cells were then split into two populations and maintained at logarithmic growth ( 500,000 cells / ml ) each day for 14 days . after 14 days of growth
genomic dna was extracted for all 3 time points separately following qiagen s blood maxi kit protocol .
a previously designed 4 sgrna / gene crispr - cas9 library was used targeting 5 ends of conserved exons with sgrnas varying in length between 19 and 25 base - pairs .
the library was generated first by infecting k562 cells with a sffv - cas9-bfp vector to create a stably expressed cas9 cell line .
we then infected the lentiviral genome - wide sgrna library into approximately 120 million cells following the same protocol as the genome - wide shrna library to maintain at least 1,000-fold representation in cells .
infected cells were selected with puromycin ( 0.7 g / ml , sigma ) for 3 days .
percentage of mcherry positive cells was measured by flow cytometry ( bd accuri c6 ) .
selected cells were spun out of selection and into normal rpmi 1640 media . at t0
cells were then split into two populations and grown for 14 days , maintaining logarithmic growth ( 500,000 cells / ml ) each day . after 14 days of growth
, cells were pelleted by centrifugation , and genomic dna was extracted for all three time samples separately following qiagen s blood maxi kit instructions .
data from previous shrna screen for modifiers of ricin toxicity were obtained as pre- computed hairpin - level enrichments averaged from two replicates , pooled data was analyzed with castle , and compared to signed , log - transformed published results ( supplementary data 5 ) .
count data for two replicates from a previous crispr / cas9 cutting screen for lps - induced tnf expression were obtained from the authors , analyzed with castle , and compared to signed , log - transformed published deseq results ( supplementary data 6 ) .
data from previous crispri and crispra screens were obtained as pre - computed guide - level enrichment averaged from two replicates , analyzed with castle , and compared to published results ( supplementary data 7 ) .
where available , positive and negative results from published low - throughput validations are also presented .
trimmed sequences were aligned to the library using bowtie with zero mismatches tolerated . all alignment from multi - mapped reads were used .
enrichment of individual hairpins was calculated as a median - normalized log - ratio of fraction of counts , as previously described .
raw count files are available ( supplementary data 2 ) , and raw fastq files have been deposited at the sequence read archive ( accession srp072806 ) and bioproject ( accession prjna317269 ; http://www.ncbi.nlm.nih.gov/bioproject/prjna317269 ) .
briefly , we built cas9 high throughput maximum likelihood estimator ( castle ) that uses an empirical bayesian framework to account for multiple sources of variability , including reagent efficacy and off - target effects ( for more complete description , see supplementary methods ) .
for each gene , we have the phenotypes of multiple targeting reagents . from this , and the phenotypes of negative controls , we obtain an effect size estimate for each gene and an associated log - likelihood ratio .
in the figures we present this as the castle score , which is twice the log - likelihood ratio , signed to match the effect size .
all screens are analyzed with the same parameters , and no optimization was performed using the gold standard or related sets .
these , along with a complete screen analysis pipeline , are available at https://bitbucket.org/dmorgens/castle . for comparison
, riger was implemented with default settings on precomputed element enrichments with gene - e ( http://www.broadinstitute.org/cancer/software/gene-e/ ) .
rsa was implemented with python scripts available from http://carrier.gnf.org / publications / rsa/. mageck was implemented with default settings with software available from https://sourceforge.net / projects / mageck/. hitselect was implemented with default settings with software available from https://sourceforge.net / projects / hitselect/. median and highest effect heuristics as well as the mw test were implemented with custom python scripts .
go terms were generated using gorilla available at http://cbl-gorilla.cs.technion.ac.il/ by ranking genes from highest confidence negative growth phenotypes to lowest confidence genes to highest confidence positive growth phenotypes . for direct comparison to the 4 sgrna per gene crispr / cas9 library , the 25
hairpin per gene library rnai library was down - sampled to 4 shrnas per gene .
hairpins were ranked according to their original computational design and the top four unique shrnas were used as well as all negative control shrnas .
note that this is independent of the data set used here and represents the 4 shrna library that would have been designed .
essential gene predictions were then repeated using this reduced shrna library as above ( supplementary fig .
gene sets were defined by 10% fpr cutoff for cas9 , shrna , and combination screens .
cas9 and shrna unique gene sets were defined as genes under the 10% fpr cutoff in the cas9 or shrna screen but neither the other nor the combination screen .
an overlap set was defined as genes under the 10% fpr cutoff for both cas9 and shrna screens .
public rna - seq data ( accession encff934ybo ) for the k562 cell line was obtained from encode experiment encsr000aem .
genes were filtered for fpkm greater than one and successful mapping to genes present in our libraries , leaving expression data for ~7,000 genes .
genes were then ranked from highest to lowest expressed and binned in increments of 500 .
the number of genes in each bin were counted for each gene set and normalized by the total number of genes in each gene set present in the rna - seq data .
was then graphed versus the mean log(fpkm ) value for each bin ( supplementary figure 11a , b ; supplementary data 10 ) .
yeast essential genes annotated as viable or inviable as well as their human homologs were obtained from the saccharomyces genome database .
the number of homologs in each gene set were counted for both inviable and viable annotations , and the fraction inviable is presented ( supplementary figure 11c , d ; supplementary data 11 ) .
p - values are calculated using fisher s exact test . the total fraction of all homologs mapped from inviable yeast genes is also presented .
cell culture performed as previously described . briefly , k562 cells ( atcc ) were cultured in rpmi 1640 ( gibco ) media and supplemented with 10% fbs ( hyclone ) , penicillin ( 10,000 i.u./ml ) , streptomycin ( 10,000 ug / ml ) , and l - glutamine ( 2 mm )
. cells were grown in log phase during all biological assays by returning the population to 500,000 cells / ml each day .
k562 cells were maintained in a controlled humidified incubator at 37 c , with 5% co2 .
briefly , to generate sufficient lentivirus to infect the genome - wide shrna library into k562 cells , we plated 293 t cells on 15 cm tissue culture plates .
293 t cells were transfected with third generation packaging plasmids and shrna encoding vectors . after 48 hrs and 72 hrs of incubation , lentivirus was harvested .
we filtered the pooled lentivirus through a 0.45 m pvdf filter ( millipore ) to remove any cellular debris .
approximately 560 million k562 cells were infected with our next - generation genome - wide lentiviral shrna library to maintain roughly 1,000-fold coverage of the shrna library after selection .
infected cells grew for 3 days before selecting the cells with puromycin ( 0.7 g / ml , sigma ) .
after three days of selection , infection efficiency was monitored using flow cytometry ( bd accuri c6 ) .
once the cells reached 90100% mcherry positive cells , they were spun out of selection and allowed to recover in normal rpmi 1640 media . at t0
, 500 million cells were pelleted by centrifugation ( 300 g for 5 min ) .
cells were then split into two populations and maintained at logarithmic growth ( 500,000 cells / ml ) each day for 14 days . after 14 days of growth
genomic dna was extracted for all 3 time points separately following qiagen s blood maxi kit protocol .
a previously designed 4 sgrna / gene crispr - cas9 library was used targeting 5 ends of conserved exons with sgrnas varying in length between 19 and 25 base - pairs .
the library was generated first by infecting k562 cells with a sffv - cas9-bfp vector to create a stably expressed cas9 cell line .
we then infected the lentiviral genome - wide sgrna library into approximately 120 million cells following the same protocol as the genome - wide shrna library to maintain at least 1,000-fold representation in cells .
infected cells were selected with puromycin ( 0.7 g / ml , sigma ) for 3 days .
percentage of mcherry positive cells was measured by flow cytometry ( bd accuri c6 ) .
selected cells were spun out of selection and into normal rpmi 1640 media . at t0
cells were then split into two populations and grown for 14 days , maintaining logarithmic growth ( 500,000 cells / ml ) each day . after 14 days of growth
, cells were pelleted by centrifugation , and genomic dna was extracted for all three time samples separately following qiagen s blood maxi kit instructions .
data from previous shrna screen for modifiers of ricin toxicity were obtained as pre- computed hairpin - level enrichments averaged from two replicates , pooled data was analyzed with castle , and compared to signed , log - transformed published results ( supplementary data 5 ) .
count data for two replicates from a previous crispr / cas9 cutting screen for lps - induced tnf expression were obtained from the authors , analyzed with castle , and compared to signed , log - transformed published deseq results ( supplementary data 6 ) .
data from previous crispri and crispra screens were obtained as pre - computed guide - level enrichment averaged from two replicates , analyzed with castle , and compared to published results ( supplementary data 7 ) .
where available , positive and negative results from published low - throughput validations are also presented .
trimmed sequences were aligned to the library using bowtie with zero mismatches tolerated . all alignment from multi - mapped reads were used .
enrichment of individual hairpins was calculated as a median - normalized log - ratio of fraction of counts , as previously described .
raw count files are available ( supplementary data 2 ) , and raw fastq files have been deposited at the sequence read archive ( accession srp072806 ) and bioproject ( accession prjna317269 ; http://www.ncbi.nlm.nih.gov/bioproject/prjna317269 ) .
briefly , we built cas9 high throughput maximum likelihood estimator ( castle ) that uses an empirical bayesian framework to account for multiple sources of variability , including reagent efficacy and off - target effects ( for more complete description , see supplementary methods ) . for each gene
, we have the phenotypes of multiple targeting reagents . from this , and the phenotypes of negative controls , we obtain an effect size estimate for each gene and an associated log - likelihood ratio .
in the figures we present this as the castle score , which is twice the log - likelihood ratio , signed to match the effect size .
all screens are analyzed with the same parameters , and no optimization was performed using the gold standard or related sets .
these , along with a complete screen analysis pipeline , are available at https://bitbucket.org/dmorgens/castle . for comparison
, riger was implemented with default settings on precomputed element enrichments with gene - e ( http://www.broadinstitute.org/cancer/software/gene-e/ ) .
rsa was implemented with python scripts available from http://carrier.gnf.org / publications / rsa/. mageck was implemented with default settings with software available from https://sourceforge.net / projects / mageck/. hitselect was implemented with default settings with software available from https://sourceforge.net / projects / hitselect/. median and highest effect heuristics as well as the mw test were implemented with custom python scripts .
go terms were generated using gorilla available at http://cbl-gorilla.cs.technion.ac.il/ by ranking genes from highest confidence negative growth phenotypes to lowest confidence genes to highest confidence positive growth phenotypes . for direct comparison to the 4 sgrna per gene crispr / cas9 library , the 25
hairpin per gene library rnai library was down - sampled to 4 shrnas per gene .
hairpins were ranked according to their original computational design and the top four unique shrnas were used as well as all negative control shrnas .
note that this is independent of the data set used here and represents the 4 shrna library that would have been designed .
essential gene predictions were then repeated using this reduced shrna library as above ( supplementary fig .
gene sets were defined by 10% fpr cutoff for cas9 , shrna , and combination screens .
cas9 and shrna unique gene sets were defined as genes under the 10% fpr cutoff in the cas9 or shrna screen but neither the other nor the combination screen .
an overlap set was defined as genes under the 10% fpr cutoff for both cas9 and shrna screens .
public rna - seq data ( accession encff934ybo ) for the k562 cell line was obtained from encode experiment encsr000aem .
genes were filtered for fpkm greater than one and successful mapping to genes present in our libraries , leaving expression data for ~7,000 genes .
genes were then ranked from highest to lowest expressed and binned in increments of 500 .
the number of genes in each bin were counted for each gene set and normalized by the total number of genes in each gene set present in the rna - seq data .
this fraction of essential genes for each gene set was then graphed versus the mean log(fpkm ) value for each bin ( supplementary figure 11a , b ; supplementary data 10 ) .
yeast essential genes annotated as viable or inviable as well as their human homologs were obtained from the saccharomyces genome database .
the number of homologs in each gene set were counted for both inviable and viable annotations , and the fraction inviable is presented ( supplementary figure 11c , d ; supplementary data 11 ) .
p - values are calculated using fisher s exact test . the total fraction of all homologs mapped from inviable yeast genes is also presented .
distribution of targeting and control elements . ( a ) distribution of negative controls for a single replicate of cas9 and shrna screens .
( b , c ) distribution of targeting elements is shown in meta - gene plots for the top 50 ( b ) enriched and ( c ) disenriched genes found in a single replicate of the cas9 and shrna screens as identified by castle . to normalize
, the enrichment of each individual element was divided by the effect size estimate for the gene generated by castle .
( a  d ) enrichment of targeting elements and estimated effect size is shown for the top four disenriched genes from cas9 data from a single replicate .
( a  d ) enrichment of targeting elements and estimated effect size is shown for the top four disenriched genes from shrna data from a single replicate .
the unknown relationship between gene dosage and measured phenotype as well as the unknown distribution of shrna and cas9 efficacies restricts the predicted effect size of reagents to a bounded region , marked as the blue shaded region , between 0 and the maximum effect i , marked by the dotted line . some fraction ( 1 ) of the reagents
the phenotype observed is thus the true effect obscured by noise , which is estimated using the distribution of non - targeting controls .
the likelihood of models for different values of i and  are calculated and by marginalizing  the most likely effect size is selected .
a likelihood ratio is then calculated by comparing to a null model where i is zero .
( a ) results are shown for a previously published shrna screen for ricin sensitivity reanalyzed with castle and compared to published results based on a mw test .
( b ) previous crispr / cas9 deletion screen for lps - induced tnf expression in primary mouse bone - marrow derived dendritic cells , analyzed with castle and the published deseq results .
( c ) previous crispri screen for sensitivity to the fusion toxin ctx - dta , analyzed with castle versus the average of the top three sgrna effects .
( d ) previous crispra screen for sensitivity to the fusion toxin ctx - dta , analyzed with castle versus the average of the top three sgrna effects .
( a  d ) roc curves indicate screen performance in identifying essential genes from changing composition between the plasmid library and two weeks growth .
true positive rates and false positive rates are calculated using a previously established gold standard set of essential and nonessential genes .
genes are ranked by likelihood to be essential using the indicated methods , including castle .
highest effect heuristic was calculated by ranking the genes according to their most disenriched element .
data is shown from single replicates of the ( a , c ) cas9 and ( b , d ) shrna screens for ( a , b ) replicate 1 and ( c , d ) replicate 2 . supplementary figure 7 .
( a ) roc curves from combination of different replicates of cas9 and shrna using castle .
roc curves indicate screen performance in identifying essential genes from changing composition between the plasmid library and two weeks growth .
true positive rates and false positive rates are calculated using a previously established gold standard set of essential and nonessential genes .
a large positive castle score indicates a high confidence increase in growth rate , while a highly negative castle indicates a high confidence decrease in growth rate , i.e. gene essentiality .
the graphs compare replicate measurements of likelihood ratio between plasmid and t14 of the combination score based on replicates 1 for cas9 and shrna and replicates 2 for cas9 and shrna .
( a ) roc curves indicate screen performance in identifying essential genes by comparing the library composition between the plasmid library and cells after two weeks growth .
roc curves for cas9 ( red ) and shrna ( blue ) screens based on duplicate data combined using castle .
alternatively , data from single replicates of both cas9 and shrna screens were combined using castle ( purple ) .
( b ) the number of essential genes at 10% false positive rate and their overlap based on the duplicate data from cas9 and shrna screens , as well as combination of a single replicate from both screens .
( c ) precision recall curve for cas9 , shrna , and combination data using castle .
results from the 25 shrna library were downsampled by only including four hairpins per gene , selected by previous computational ranking .
( a ) roc curves indicate screen performance in identifying essential genes by comparing the library composition between the plasmid library and cells after two weeks growth .
( b ) the number of essential genes at 10% false positive rate and their overlap based on the duplicate data from cas9 and shrna screens , as well as combination of a single replicate from both screens .
( c ) comparison of castle scores derived from castle between single replicates of cas9 and shrna data .
( d ) adjusted p - values for select go terms for shrna and cas9 screens as well as for data from both screens combined with castle . supplementary figure 10 .
a large positive castle score indicates a high confidence increase in growth rate , while a highly negative castle score indicates a high confidence decrease in growth rate , i.e. gene essentiality .
the graphs compare replicate measurements of castle scores between plasmid and t14 for ( a ) cas9 and ( b ) shrna screens .
castle scores in different time - frames for ( c ) cas9 and ( d ) shrna screens .
genesets are defined for cas9 , shrna , and combination by a 10% fpr cutoff .
genesets are defined for cas9-combo and shrna - combo by the genes present in cas9 or shrna set and not in the combination set .
overlap set is defined as genes present in both the cas9 and shrna set ( see supplementary fig .
( a , b ) ~7,000 genes with detectable expression in k562 were binned by expression .
the fraction of genes identified as essential in each bin is reported versus the average expression level of the bin .
( c , d ) fraction of genes that are homologs of essential yeast genes versus genes that are homologs of nonessential yeast genes .
 we compare the ability of shrna and crispr / cas9 screens to identify essential genes in the human chronic myelogenous leukemia cell line k562 . we find that the precision of the two libraries in detecting essential genes is similar and that combining data from both screens improves performance . 
 notably , results from the two screens show little correlation , which can be partially explained by identification of distinct essential biological processes with each technology . 
Methods
Cell Culture
Genome-wide shRNA screen
Genome-wide CRISPR-Cas9 screen
Analysis of previous screens
Analysis of screen results
Analysis of gene expression data and yeast essentials
Supplementary Material