Uri Keich

School of Mathematics and Statistics F07
University of Sydney NSW 2006

+61 2 9351 2307

Research Interest

My current research is motivated by problems in computational biology though I occasionally drift toward more general problems of statistical nature. It includes collaborative research on actual biological problems such as the mapping of sequence motifs involved in DNA replication initiation, the development of tools for the discovery and analysis of sequence motifs, the statistical analysis of tandem mass spectrometry data, and the design of novel computational approaches for analysis of classical statistical tests. The following couple of examples of projects I am working on will give you a better idea what I mean.

Motif Finding - The identification of transcription factor binding sites is an important step in understanding the regulation of gene expression. To address this need, many motif-finding tools (or finders) have been described that can find short sequence motifs given for example only an input set of sequences. The motifs returned by these tools are evaluated and ranked according to some measure of statistical over-representation, the most popular of which is based on the information content, or entropy. Our interest here lies mainly in analyzing the statistical significance of a finder's output. This important area has lagged considerably behind the extensive development of the finders. While our goal is to design a reliable and usable significance analysis, we also show how such analysis can be leveraged to improve the actual motif finding process. Joint work mostly with my former Ph.D. students: Niranjan Nagarajan (now a Senior Research Scientist at the Genome Institute of Singapore), Patrick Ng (now with the financial industry), and Emi Tanaka (now a research fellow at the University of Wollongong).

Study of Replication Origins - DNA replication is a fundamental process essential for cell proliferation. While the proteins involved in initiating DNA replication are essentially conserved from yeast to humans, the implicated sequence motifs that these conserved factors interact with are poorly understood outside of S. cerevisiae (baker's yeast). In collaboration with Cornell molecular biologist Bik Tye and separately with the University of Washington's Maitreya Dunham and her post-doctoral researcher Ivan Liachko we are mapping replication origins in other yeast species hoping to gain an understanding of the evolution of DNA replication origins.

Computational Statistics - Our search for an efficient and accurate computation of motif significance led us to develop a new approach for exact tests (exact tests are ones where the significance of the test is evaluated directly from the underlying distribution rather than using an approximation). Borrowing ideas from large-deviation theory, the underlying mechanism of our approach is the exact numerical calculation of the exponentially shifted characteristic function of the test statistic. Together with my former PhD student Niranjan Nagarajan we used this technique to develop faster exact algorithms for the classical multinomial goodness-of-fit test and the Mann-Whitney test. More recently we worked on extending this approach so that it can be applied much more generally.

Analysis of Tandem Mass Spectrometry Data - Recently I have been working extensively with Bill Noble from the University of Wahington on the problem of spectra identifications in a shotgun proteomics experiment, where tandem mass spectrometry is used to identify the proteins in a sample. The identification begins with associating with each of the thousands of the generated peptide fragmentation spectra an optimal matching peptide among all peptides in a candidate database. Unfortunately, the resulting list of optimal peptide-spectrum matches contains many incorrect, random matches, so it is critically important to determine how many of the top scoring matches are expected to be incorrect. This topic is is intimately related to the extremely popular notion of false discovery rate (FDR).

Current and Recent Teaching:

STAT 2911 - Probability and Statistical Models (Advanced): Semester 1, 2016 (past: 2009-2015)

STAT 3914 - Applied Statistics (Advanced): Semester 2, 2015 (past: 2010-2011, 2013-2014)

STAT 3014 - Applied Statistics (Advanced): Semester 2, 2015 (past: 2013-2014)

MATH 1907 - Mathematics (Special Studies Program) B: (past: 2010)

MSH8 - Statistical Methods in Bioinformatics: Semester 2, 2015 (past: 2009-2014)


R code of aFFT-C and sisFFT – aFFT-C accurately convolves two non-negative vectors (see Accurate pairwise convolutions of non-negative vectors via FFT below), and sisFFT accurately computes tail probabilities (p-values) of a sum of iid lattice valued random variables (submitted).

Python code of aFFT-C and sisFFT – Python version of above R code

ALICO – alignment constrained sampling

GIMSAN – a novel tool for de novo motif finding that includes a reliable significance analysis

SADMAMA (new version 17/2/2010) – computational tool for motif scanning and for detection of significant variation in binding affinity across two sets of sequences

The FAST package – Fourier transform based Algorithms for Significance Testing of ungapped multiple alignments

csFFT/sFFT – computing the p-value of the information content (entropy score) of a sequence motif

BagFFT – computing the exact p-value of the llr statistic for multinomial goodness-of-fit test


Ph.D. in Mathematics, Courant Institute, New York University
Thesis title: Stationary Approximations to Non-Stationary Stochastic Processes.
Advisor: Prof. H . P. McKean

M.Sc. in Mathematics, Department of Mathematics, Technion - Israel Institute of Technology
Thesis title: A Generalization of the "Ahlswede Daykin Inequality".
Advisor: Prof. R. Aharoni

B.Sc. in Computer Science and Mathematics, Hebrew University of Jerusalem

Professional Experience:

2009 - present:
Associate Professor in the School of Mathematics and Statistics at the University of Sydney (Senior Lecturer 2009-2015)

2003 - 2009:
Assistant Professor at the Computer Science Department of Cornell University
2001 - 2003:
Project scientist at the Department of Computer Science and Engineering of the University of California, San Diego
1999 - 2000:
Assistant Professor at the Department of Mathematics of the University of California, Riverside
1996 - 1999:
Von Karman Instructor at the Applied Mathematics Department of the California Institute of Technology
1991 - 1996:
Research and Teaching assistant at the Courant Institute of New York University


Wilson H. and Keich U. Accurate pairwise convolutions of non-negative vectors via FFT. Computational Statistics & Data Analysis, 101: 300-315, 2016 (paper).

Keich U., Kertesz-Farkas A., Noble WS. Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics. Journal of Proteome Research, 14 (8): 3148-61, 2015 (paper).

Kertesz-Farkas A., Keich U, Noble WS. Tandem Mass Spectrum Identification via Cascaded Search. Journal of Proteome Research, 14 (8): 3027-38, 2015 (paper).

Manescu D. and Keich U. A symmetric length-aware enrichment test. Best Paper Award, RECOMB 2015, LNBI 9029: 224–242, 2015 (preprint).

Keich U. and Noble WS. On the Importance of Well-Calibrated Scores for Identifying Shotgun Proteomics Spectra. Journal of Proteome Research, 14(2):1147–1160, 2015 (paper).

Tanaka E., Bailey TL., Keich U. Improving MEME via a two-tiered significance analysis. Bioinformatics, 30(14): 1965-1973, 2014 (paper).

Liachko I., Youngblood RA., Keich U., Dunham MJ. High-resolution mapping, characterization, and optimization of autonomously replicating sequences in yeast. Genome Research, 23(4):698-704, 2013 (paper) (co-corresponding author).

Liachko I., Tanaka E., Cox K., Chung SC., Yang L., Seher A., Hallas L., Cha E., Kang G., Pace H., Barrow J., Inada M., Tye BK., Keich U. Novel Features of ARS Selection in Budding Yeast Lachancea kluyveri. BMC Genomics, 12:633, 2011 (abstract).

Tanaka E., Bailey T., Grant CE., Noble WS., Keich U. Improved similarity scores for comparing motifs. Bioinformatics, 27(12):1603-9, 2011 (abstract).

Gupta N., Bandeira N., Keich U., Pevzner PA. Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong. Journal of The American Society for Mass Spectrometry, Vol. 22, No. 7: 1111 - 1120, 2011(paper).

Ng P,. and Keich U. Alignment Constrained Sampling. Journal of Computational Biology, Vol. 18: No. 2, 2011 (paper).

Bhaskar A,. and Keich U. Confidently Estimating the Number of DNA Replication Origins. Statistical Applications in Genetics and Molecular Biology, Vol. 9: Iss. 1, Article 28, 2010 (paper).

Liachko I., Bhaskar A., Li C., Chung S.C.C., Tye B.K., and Keich U. A Comprehensive Genome-Wide Map of Autonomously Replicating Sequences in a Naive Genome. PLoS Genetics, May 2010 Issue. (paper).

Oliver H.F., Orsi R.H., Ponnala L., Keich U., Wang W., Sun Q., Cartinhour S.W., Filiatrault M.J., Wiedmann M., and Boor K.J. Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. BMC Genomics, 10:641, 2009. (paper).

Nagarajan N. and Keich U. Reliability and efficiency of algorithms for computing the significance of the Mann-Whitney test. Computational Statistics, 24(4):605-622, 2009. (paper).

Ng P. and Keich U. Factoring local sequence composition in motif significance analysis. Genome Informatics, 21:15-26, 2008. (preprint).

Keich U., Gao H., Garretson JS., Bhaskar A., Liachko I., Donato J., Tye B. Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast. BMC Bioinformatics, 9:372, 2008. (paper).

Ng P. and Keich U. GIMSAN: a Gibbs motif finder with significance analysis. Bioinformatics, 24(19):2256-7, 2008. (paper).

Keich U. and Ng P. A conservative parametric approach to motif significance analysis. Genome Informatics, 19:61-72, 2007. (preprint)

Nagarajan N. and Keich U. FAST: Fourier transform based Algorithms for Significance Testing of ungapped multiple alignments. Bioinformatics, 24(4):577-8, 2008. (paper).

Ng P., Nagarajan N., Jones N., and Keich U. Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone. Bioinformatics, 22(14):e393-401, ISMB 2006. (preprint)

Nagarajan N., Ng P., Keich U. Refining motif finders with E-value calculations. Proceedings of the 3rd RECOMB Satellite Workshop on Regulatory Genomics, Singapore. 73-84, 2006. (preprint)

Keich U., Nagarajan N. A fast and numerically robust method for exact multinomial goodness-of-fit test. Journal of Computational and Graphical Statistics, , 15(4):779-802, 2006. (preprint)

Nagarajan N., Jones N., and Keich U. Computing the p-value of the information content from an alignment of multiple sequences. Bioinformatics, Vol. 21, Suppl 1, i311-i318, ISMB 2005. (preprint) (Erratum)

Buhler J., Keich U., Sun Y. Designing Seeds for Similarity Search in Genomic DNA. Journal of Computer and System Sciences, Volume 70, Issue 3, May 2005, Pages 342-363. (preprint)

Keich U., and Nagarajan N. A Faster Reliable Algorithm to Estimate the p-Value of the Multinomial llr Statistic. Proceedings of the 4th International Workshop on Algorithms in Bioinformatic (WABI 2004), September 2004, Bergen, Norway. (preprint)

Keich U. sFFT: a faster accurate computation of the p-value of the entropy score. Journal of Computational Biology, Volume 12, Number 4, May 2005, Pages 416-430. (preprint)

Zhi D., Keich U., Pevzner P., Heber S., and Tang H. Correcting base-assignment errors in repeat regions of shotgun assembly. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(1):54-64, (2007). (preprint)

Keich U., Li M., Ma B., and Tromp J. On Spaced Seeds for Similarity Search. Discrete Applied Mathematics, 138(3):253--263. 2004. (preprint)

Buhler J., Keich U., Sun Y. Designing Seeds for Similarity Search in Genomic DNA. Proceedings of the Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB-2003), April 2003, Berlin, Germany. (preprint)

Eskin E., Keich U., Gelfand M.S., Pevzner P.A. Genome-Wide Analysis of Bacterial Promoter Regions. Proceedings of the Pacific Symposium on Biocomputing (PSB-2003), January 2003, Kaua'i, Hawaii. (preprint)

Keich U. and Pevzner, P.A. Finding motifs in the twilight zone. Bioinformatics, Vol. 18 (2002), Issue 10, 1374-1381. (preprint)

Keich U. and Pevzner P.A. Subtle motifs: defining the limits of motif finding algorithms. Bioinformatics, Vol. 18 (2002), Issue 10, 1382-1390. (preprint)

Keich U. and Pevzner P.A. Finding motifs in the twilight zone. Proceedings of the Sixth Annual International Conference on Research in Computational Molecular Biology (RECOMB-2002), April 2002, Washington DC, USA, ACM Press. (preprint)

Keich U. A Stationary Tangent - the Discrete and Non-smooth Cases. Journal of Time Series Analysis, March 2003, vol. 24, no. 2, pp. 173-192(20). (preprint)

Cwikel M. and Keich U. Optimal decompositions for the K-functional for a couple of Banach lattices. Arkiv för Matematik, 39 (2001), No. 1, 27-64. (preprint)

Keich U. A Possible Definition of A Stationary Tangent. Stochastic Processes and Their Applications, 88 (2000), No. 1, 1-36. (preprint)

Keich U. Krein's Strings, the Symmetric Moment Problem, and Extending a Real Positive Definite Function., Communications on Pure and Applied Mathematics, 52 (1999), no. 10, 1315-1334. (preprint)

Keich U. On Lp Bounds for Kakeya Maximal Functions and the Minkowski Dimension in R2., Bulletin of the London Mathematical Society, 31 (1999), 213-221. (preprint)

Keich U. Absolute Continuity Between the Wiener and Stationary Gaussian Measures., Pacific Journal of Mathematics, Vol. 88 (1999), No. 1, 95-108. (preprint)

Keich U. The Entropy Distance Between the Wiener and Stationary Gaussian Measures., Pacific Journal of Mathematics, Vol. 88 (1999), No. 1, 109-128. (preprint)

Aharoni R. and Keich U. A Generalization of the Ahlswede Daykin Inequality., Discrete Mathematics , 152 (1996), 1-12. (preprint)