Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood.

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

Closing gaps in draft genomes is an important post processing step in genome assembly. At present, most assembled genomes contain gaps. Usually, genomes assembled from short reads or hybrid (with both short and long reads) have much more gaps than genomes assembled purely from long reads (with high coverage). A more complete genome is highly desirable since it leads to better annotation, less genotyping error and easier identification of causal variation associated with traits [1]. Several tools [2]-[4] have been developed for closing gaps on draft genomes with short reads. While these approaches have been used to close gaps, these tools still cannot close many gaps (especially those originated in more complex genomic regions, e.g. repeats).

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

Detection of KMT2A Partial Tandem Duplication in Myeloid Samples By Pacbio® Long Read Sequencing

Motivation and Objectives The output of a high throughput next generation sequencing (NGS) machine is a collection of short reads, which have to be properly assembled in order to reconstruct the original DNA sequence of the analyzed organism (Metzker, 2010; Earl, 2011). The DNA sequence assembly process is based on aligning and merging these reads for effectively reconstructing the real primary structure of the DNA sample sequence or reference genome. The use of NGS machines results in much larger sets of reads to be assembled, posing new problems for computer scientists and bioinformaticians. In particular, a relevant issue is related with the trade-off between precision of the assembly process and its computational time, stating the need for faster methods that can keep pace with the speed and volume of reads that are generated with NGS. An important step in DNA assembly is the identification of a subset of read pairs that have a high probability of being aligned sequentially in the reconstruction. Such step is often referred to as filtering, and amounts in selecting a significantly smaller subset of the initial set of read pairs (whose dimension is quadratic in the number of initial reads) that can be then processed by an alignment algorithm, usually quite time consuming. The desired effect of filtering is then to quickly filter out from the candidate set of read pairs those that would not provide a good alignment in the following phase. The computation cost of filtering should then be balanced by the speed-up obtained when a smaller set of read pairs is considered for alignment. In this work we propose and test the use of alignment free distances to evaluate the similarity between two short reads as a technique for filtering good read pairs to be assembled. The method operates in constant time in the string length and is tested in its ability to emulate, with a proper level of precision, much more time consuming methods to evaluate the similarity between short DNA sequences, such as the established Needleman-Wunsch edit distance (Needleman, Wunsch, and Christian, 1970), often used in the final step of the assembly procedure. These preliminary experiments show the efficacy of this approach for filtering the promising read pairs - eligible candidates to successfully assemble the entire genome of a given organism. Therefore, the alignment free reads filtering may significantly accelerate the assembly process without a substantial loss in accuracy of the DNA sample sequence reconstruction. Methods ODNA sequence assembly The DNA sequence assembly process is based on the alignment and merging of reads (stretch of sequences) in order to reconstruct the original primary structure of the DNA sample sequences. Given a set of sequences S = {s1, s2, …, sn}, where s ∈ S is a fragment of the primary structure of DNA (read) (e.g. s = {ATTCGA...CTGACT}), assembly is in charge of building the longest sequence from the set S where each pair of consequent reads obey certain similarity conditions. DNA read pairs filtering and Alignment Free Distance This step identifies the promising read pairs in order to reduce the amount of input data given to the real assembly algorithm. We adopted a very quick measure of the similarity between two reads, Alignment Free (AF) based distance (Vinga and Almeida, 2003). AF computes the similarity of two strings based only on the dictionary of their substrings, irrespective of their relative position. As a dictionary we considered the set of 4-mer (sequences composed of 4 different nucleotides) and then built a profile for each read composed by the relative frequencies of each 4-mer in the read. The Euclidean distance between the profiles of two reads was taken as an inverse measure of the similarity of the two reads and thus as an indication that the two reads formed a promising pair to be considered in the assembly phase. AF filtering was then used defining a proper threshold on the AF distance and discarding all the pairs that exhibited a AF distance above the threshold. Computational complexity of AF distance is a constant linearly bounded by the number of k-mers adopted and the length of the strings to be compared. Comparing with other distances: Needleman-Wunsch and “Bowtie” distance Along with AF we considered the well-established Needleman-Wunsch edit distance (NW) and compared them in their ability to identify significant pairs. This comparison was based on the computation of a sort of perfect distance computed after an alignment over an already known sequence has been performed. Such distance, referred to as Bowtie distance (BT), was obtained as follows: a. a large number of reads coming from a known sequence were considered; b. hese reads were aligned over the known sequence using the standard Bowtie algorithm (Langmead et al, 2009); c. any two reads received a maximum BT distance if their alignment did not intersect over the reference sequence, else they received a distance inversely proportional to their intersection over the sequence (e.g., they would have BT distance equal to 0 if they were aligned one on top (or inside) of the other by the Bowtie algorithm). By construction we assumed BT distance to be the reference distance, e.g., the distance that expressed the best possible alignments - being based on the knowledge of the reference sequence - and tested the correlation of AF and NW with BT; moreover, we verified the ability of AF and NW to predict that a given read pair had BT distance above or below a given threshold. Results and Discussion For our test we considered the E.Coli genome and a set of reads from this genome reads obtained by Roche 454 sequencing machine. Reads have average length of ~235 nucleotides and standard deviation of approx. 10 (the large majority of them having length in the interval 225-245). Reads were aligned with the reference sequence with Bowtie and then 100,000 were sampled at random according to their alignment along the sequence. Reads were considered both forward and reversed, giving rise to a total of 200,0002 read pairs. All 620,798 read pairs with BT distance < 1 were considered for the experiments; then, out of the remaining pairs, 233,099 were sampled at random. A total of 853,897 read pairs composed the working data set. For all these reads, NW distance and AF distance over the 4-mer were computed. AF, NW and BT distances were all normalized between 0 (maximal similarity) and 1 (maximal dissimilarity). The first interesting results was that the correlation between distances showed that AF approximates BT somehow better than NW: we obtained a correlation coefficient of 0.761 for AF and BT, compared with a smaller 0.706 when NW and BT were considered (coherently, correlation between AF and NW is 0,721). The second interesting results was obtained when we compared the ability of AF and NW to predict whether BT was above or below a given threshold. We defined a threshold predictor for a given function F2 based on function F1 and on a given pair a1, a2 as follows: if (F1 < a1) then predict (F2 < a2), else predict (F2 ≥ a2). To a given pair (a1, a2), we associated the measure of True Positive rate (TP) (percentage of cases where (F1 < a1) and (F2 < a2) and of True Negative rate (TN) (percentage of cases where (F1 ≥ a1) and (F2 ≥ a2); analogously we defined False Positive rate (FP) and False Negative rate (FN). For each (a1, a2) with both values ranging from 0 to 1, we then computed, with step 0.05, the positive and negative error rates taking AF as a predictor of BT and NW as a predictor of BT. Part of the results are summarized in the charts of Figure 1, that show for 3 different levels of a2 (0.1, 0.2, and 0.3) the precision of the predictors (y-axis) when the value of a1 is changed (x-axis), both when AF is used as a predictor of BT (charts in the first row) and when NW is used as a predictor of BT (charts in second row). Similar results are obtained also for other levels of a2, here omitted for brevity. The curves bring to light very clearly how AF is a very good threshold predictor for BT for the considered data; despite its light computational complexity, it appears to perform significantly better than the more complex NW edit distance when its ability to support a threshold predictor is considered. Acknowledgements The authors are partially supported by the FLAGSHIP ”InterOmics” project (PB.P05) funded by the Italian MIUR and CNR institutions, and by the cooperative programme 2010–2012 between the National Research Council of Italy (CNR) and the Polish Academy of Sciences (PAN). References Earl D et al (2011); Assemblathon 1: A competitive assessment of de novo short read assembly methods; Genome Research, 21 Langmead B, Trapnell C, et al (2009); Ultrafast and memory-efficient alignment of short DNA sequences to the human genome; Genome Biology, 10:R25 Metzker ML (2010); Sequencing technologies — the next generation; Nat Rev Genet., 11(1) Needleman SB, Wunsch CD. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3): 443–53 Vinga S, Almeida J (2003); Alignment-free sequence comparison—a review, Bioinformatics 19 (4), 513-523 Note: Figures and tables are available in PDF version only.

ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein–DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for further analysis. Although genomic regions enriched with mapped reads could be inferred as approximate binding regions, short read lengths (∼25–50 nt) pose challenges for determining the exact binding sites within these regions. Here, we present SISSRs (Site Identification from Short Sequence Reads), a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments. The sensitivity and specificity of SISSRs are demonstrated by applying it on ChIP-Seq data for three widely studied and well-characterized human transcription factors: CTCF (CCCTC-binding factor), NRSF (neuron-restrictive silencer factor) and STAT1 (signal transducer and activator of transcription protein 1). We identified 26 814, 5813 and 73 956 binding sites for CTCF, NRSF and STAT1 proteins, respectively, which is 32, 299 and 78% more than that inferred previously for the respective proteins. Motif analysis revealed that an overwhelming majority of the identified binding sites contained the previously established consensus binding sequence for the respective proteins, thus attesting for SISSRs’ accuracy. SISSRs’ sensitivity and precision facilitated further analyses of ChIP-Seq data revealing interesting insights, which we believe will serve as guidance for designing ChIP-Seq experiments to map in vivo protein–DNA interactions. We also show that tag densities at the binding sites are a good indicator of protein–DNA binding affinity, which could be used to distinguish and characterize strong and weak binding sites. Using tag density as an indicator of DNA-binding affinity, we have identified core residues within the NRSF and CTCF binding sites that are critical for a stronger DNA binding.

CpG methylation, a key epigenetic mark involved in gene regulation, development, and other biological processes, is commonly analyzed using Whole-Genome Bisulfite Sequencing (WGBS). However, bisulfite treatment causes significant DNA degradation. Enzymatic Methyl-seq (EM-seq) offers a short-read alternative that preserves DNA integrity but requires conversion steps, limiting its compatibility with downstream analyses. Third-generation sequencing technologies, such as Oxford Nanopore Technologies (ONT) and PacBio, enable direct detection of DNA modifications without altering the DNA, providing simultaneous genome and epigenome information. This work presents a comprehensive dataset combining long- and short-read sequencing data, including ONT, PacBio, Enzymatic Methyl-seq, and WGBS, for two agronomically relevant species: pig (Sus scrofa) and quail (Coturnix japonica). Data quality evaluation reveals high nucleotide quality scores for PacBio and short reads, robust alignment rates for long reads, and inter-method correlations in CpG methylation calling ranging from 0.76 to 0.99. This dataset is a valuable resource for training methylation callers and represents the first combined methylation dataset for these species, providing an essential benchmark for assessing emerging sequencing technologies.

Transferable linezolid resistance due to optrA, poxtA, cfr and cfr-like genes is increasingly detected in enterococci associated with animals and humans globally. We aimed to characterize the genetic environment of optrA in linezolid-resistant Enterococcus faecalis isolates from Scotland. Six linezolid-resistant E. faecalis isolated from urogenital samples were confirmed to carry the optrA gene by PCR. Short read (Illumina) sequencing showed the isolates were genetically distinct (>13900 core SNPs) and belonged to different MLST sequence types. Plasmid contents were examined using hybrid assembly of short and long read (Oxford Nanopore MinION) sequencing technologies. The optrA gene was located on distinct plasmids in each isolate, suggesting that transfer of a single plasmid did not contribute to optrA dissemination in this collection. pTM6294-2, BX5936-1 and pWE0438-1 were similar to optrA-positive plasmids from China and Japan, while the remaining three plasmids had limited similarity to other published examples. We identified the novel Tn6993 transposon in pWE0254-1 carrying linezolid (optrA), macrolide (ermB) and spectinomycin [ANT(9)-Ia] resistance genes. OptrA amino acid sequences differed by 0–20 residues. We report multiple variants of optrA on distinct plasmids in diverse strains of E. faecalis . It is important to identify the selection pressures driving the emergence and maintenance of resistance against linezolid to retain the clinical utility of this antibiotic.

Repetitive sequences and genomic variations mediated by repetitive sequences remain a barrier to our understanding of the function and dynamics of the human genome. Only recently have high throughput short and long reads sequencing provided an opportunity to overcome these challenges. In this thesis, I investigate the distribution, evolution and polymorphism of two types of repeats: subtelomeric and inverted repeats. I also investigate two distinct aspects of genomic mutations related to these repetitive sequences: inversion polymorphisms, and structural variation in chromosome subtelomeric regions.In the first chapter, I provide an overview of our current knowledge, as well as gaps in our understanding of the structure of the human genome. I also provide an overview of the sequencing technologies I will use in this thesis.In the second chapter, I show the performance of inversion detection by short read sequencing. Next, I develop and apply an approach for using long read nanopore sequencing data to detect inversion polymorphism. I show that using long reads it is possible to detect inversions mediated by long inverted repeats which are invisible to paired-end short read sequencing.In the third chapter, I investigate the distribution of inverted repeats in the human genome. I combined this information with existing maps of inversion polymorphisms to investigate the role of inverted repeats in mediating formation of inversions.In the fourth chapter, I apply BioNano data to anchor and extend chromosome ends. This analysis uncovers a surprising level of polymorphism in human chromosome end extension, suggesting an ongoing process of terminal loss and extension. I confirm these chromosome end extension using both nanopore and pacbio long read sequence data.In the fifth chapter, I investigate the evolution of chromosome termini. I identify ancient chromosome terminal ends by investigation of subtelomeric interstitial telomere sequences across multiple mammalian species. This analysis provides an estimate of the rate at which chromosome ends have extended.

Antibiotic resistance has escalated as a significant problem of broad public health significance. Regular surveillance of antibiotic resistance genes (ARGs) in microbes and metagenomes from human, animal and environmental sources is vital to understanding ARGs’ epidemiology and foreseeing the emergence of new antibiotic resistance determinants. Whole-genome sequencing (WGS)-based identification of the microbial ARGs using antibiotic resistance databases and in silico prediction tools can significantly expedite the monitoring and characterization of ARGs in various niches. The major hindrance to the annotation of ARGs from WGS data is that most genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we describe an insilicoBacterial Antibiotic Resistance scan (BacARscan) (http://proteininformatics.org/mkumar/bacarscan/) that can detect, predict and characterize ARGs in -omics datasets, including short sequencing, reads, and fragmented contigs. Benchmarking on an independent non-redundant dataset revealed that the performance of BacARscan was better than other existing methods, with nearly 92% Precision and 95% F-measure on a combined dataset of ARG and non-ARG proteins. One of the most notable improvements of BacARscan over other ARG annotation methods is its ability to work on genomes and short-reads sequence libraries with equal efficiency and without any requirement for assembly of short reads. Thus, BacARscan can help monitor the prevalence and diversity of ARGs in microbial populations and metagenomic samples from animal, human, and environmental settings. The authors intend to constantly update the current version of BacARscan as and when new ARGs are discovered. Executable versions, source codes, sequences used for development and usage instructions are available at (http://www.proteininformatics.org/mkumar/bacarscan/downloads.html) and GitHub repository (https://github.com/mkubiophysics/BacARscan).

BackgroundMicroevolution is the study of short-term changes of alleles within a population and their effects on the phenotype of organisms. The result of the below-species-level evolution is heterogeneity, where populations consist of subpopulations with a large number of structural variations. Heterogeneity analysis is thus essential to our understanding of how selective and neutral forces shape bacterial populations over a short period of time. The Solexa Genome Analyzer, a next-generation sequencing platform, allows millions of short sequencing reads to be obtained with great accuracy, allowing for the ability to study the dynamics of the bacterial population at the whole genome level. The tool referred to as GenHtr was developed for genome-wide heterogeneity analysis.ResultsFor particular bacterial strains, GenHtr relies on a set of Solexa short reads on given bacteria pathogens and their isogenic reference genome to identify heterogeneity sites, the chromosomal positions with multiple variants of genes in the bacterial population, and variations that occur in large gene families. GenHtr accomplishes this by building and comparatively analyzing genome-wide heterogeneity genotypes for both the newly sequenced genomes (using massive short-read sequencing) and their isogenic reference (using simulated data). As proof of the concept, this approach was applied to SRX007711, the Solexa sequencing data for a newly sequenced Staphylococcus aureus subsp. USA300 cell line, and demonstrated that it could predict such multiple variants. They include multiple variants of genes critical in pathogenesis, e.g. genes encoding a LysR family transcriptional regulator, 23 S ribosomal RNA, and DNA mismatch repair protein MutS. The heterogeneity results in non-synonymous and nonsense mutations, leading to truncated proteins for both LysR and MutS.ConclusionGenHtr was developed for genome-wide heterogeneity analysis. Although it is much more time-consuming when compared to Maq, a popular tool for SNP analysis, GenHtr is able to predict potential multiple variants that pre-exist in the bacterial population as well as SNPs that occur in the highly duplicated gene families. It is expected that, with the proper experimental design, this analysis can improve our understanding of the molecular mechanism underlying the dynamics and the evolution of drug-resistant bacterial pathogens.

Transposable elements (TE) are important components in plant genomes. They are abundant, not only in flax but also in a wide range of species, varying and accounting for 10–85% in different genomes. In genome assemblies and variant identification, repeats have always posed technical challenges, particularly when using short sequence reads. However, long-read sequencing technologies, such as those of Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio), have the potential to overcome these limitations, helping to resolve issues surrounding repeats in genome assembly and variant calling. The use of optimized bioinformatics tools and the ability to handle repeats are also important to avoid assembly errors or false-positive variant calls. Hence, accurate repeat identification is essential for downstream analysis of structural variants and gene prediction as well as for comparative and evolutionary analyses. This review provides a brief overview of repetitive elements and their significance, a description of available tools for discovery, and highlights a case study of repeat analysis of the flax genome.KeywordsRepeat DNA sequencesCultivated and wild flaxTransposable elementsLong and short reads

With the decrease in cost and increase in output of whole-genome shotgun technologies, many metagenomic studies are utilizing this approach in lieu of the more traditional 16S rRNA amplicon technique. Due to the large number of relatively short reads output from whole-genome shotgun technologies, there is a need for fast and accurate short-read OTU classifiers. While there are relatively fast and accurate algorithms available, such as MetaPhlAn, MetaPhyler, PhyloPythiaS, and PhymmBL, these algorithms still classify samples in a read-by-read fashion and so execution times can range from hours to days on large datasets. We introduce WGSQuikr, a reconstruction method which can compute a vector of taxonomic assignments and their proportions in the sample with remarkable speed and accuracy. We demonstrate on simulated data that WGSQuikr is typically more accurate and up to an order of magnitude faster than the aforementioned classification algorithms. We also verify the utility of WGSQuikr on real biological data in the form of a mock community. WGSQuikr is a Whole-Genome Shotgun QUadratic, Iterative, -mer based Reconstruction method which extends the previously introduced 16S rRNA-based algorithm Quikr. A MATLAB implementation of WGSQuikr is available at: http://sourceforge.net/projects/wgsquikr.

BackgroundDNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.ResultsHere, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences’ AVITI and Illumina’s NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina’s NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms’ respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.ConclusionThese results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

T-cell receptor (TCR) diversity in peripheral blood has not yet been fully profiled with sequence level resolution. Each T-cell clonotype expresses a unique receptor, generated by somatic recombination of TCR genes and the enormous potential for T-cell diversity makes repertoire analysis challenging. We developed a sequencing approach and assembly software (immuno-SSAKE or iSSAKE) for profiling T-cell metagenomes using short reads from the massively parallel sequencing platforms. Models of sequence diversity for the TCR beta-chain CDR3 region were built using empirical data and used to simulate, at random, distinct TCR clonotypes at 1-20 p.p.m. Using simulated TCRbeta (sTCRbeta) sequences, we randomly created 20 million 36 nt reads having 1-2% random error, 20 million 42 or 50 nt reads having 1% random error and 20 million 36 nt reads with 1% error modeled on real short read data. Reads aligning to the end of known TCR variable (V) genes and having consecutive unmatched bases in the adjacent CDR3 were used to seed iSSAKE de novo assemblies of CDR3. With assembled 36 nt reads, we detect over 51% and 63% of rare (1 p.p.m.) clonotypes using a random or modeled error distribution, respectively. We detect over 99% of more abundant clonotypes (6 p.p.m. or higher) using either error distribution. Longer reads improve sensitivity, with assembled 42 and 50 nt reads identifying 82.0% and 94.7% of rare 1 p.p.m. clonotypes, respectively. Our approach illustrates the feasibility of complete profiling of the TCR repertoire using new massively parallel short read sequencing technology. ftp://ftp.bcgsc.ca/supplementary/iSSAKE.