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Mariadassou, M., Nouvel, L.-X., Constant, F., Morgavi,
D., Rault, L., Barbey, S., Helloin, E., Rue, O., Schbath, S., Launay, F., Sandra, O., Lefebvre, R., Le Loir, Y., Germon, P., Citti, C. and Even, S.
(2023).
Microbiota members from body sites of dairy cows are largely shared
within individual hosts throughout lactation but sharing is limited in the herd.
Animal Microbiome.
5.
[ DOI ]
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[51]
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Bize, A., Midoux, C., Mariadassou, M., Schbath, S., Forterre, P. and da Cunha, V.
(2021).
Exploring short k-mer profiles in cells and mobile elements from
archaea highlights the major influence of both the ecological niche and evolutionary history.
BMC Genomics.
22.
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[50]
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Aubert, J., Schbath, S. and Robin, S.
(2021).
Model-based biclustering for overdispersed count data with application in microbial ecology.
Methods in Ecology and Evolution.
12 1050-1061.
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[49]
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Sultan, I., Fromion, V., Schbath, S. and Nicolas, P.
(2020).
Statistical modelling of bacterial promoter sequences for regulatory motif discovery with
the help of transcrriptome data: application to listeria monocytogenes.
Journal of the Royal Society Interface.
17.
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[48]
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Hurel, J., Schbath, S., Bougeard, S., Rolland, M., Petrillo, M. and Touzain F.
(2020).
DUGMO: tool for the detection of unknown genetically modified organisms with high-throughput sequencing data for pure bacterial samples
BMC Bioinformatics.
21.
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[47]
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Benoit, G., Mariadassou, M., Robin, S., Schbath, S., Peterlongo, P. and Lemaitre, C.
(2019).
SimkaMin: fast and resource frugal de novo comparative metagenomics.
Bioinformatics.
36.
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[46]
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Benoit, G., Peterlongo, P., Mariadassou, M., Drezen, E., Schbath, S., Lavenier, D. and Lemaitre, C.
(2016).
Multiple comparative metagenomics using multiset k-mer counting.
PeerJ Computer Science.
e94.
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[45]
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Massip, F., Sheinman, M., Schbath, S. and Arndt, P.F.
(2016).
Comparing the Statistical Fate of Paralogous and Orthologous Sequences.
Genetics.
204.
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[44]
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Massip, F., Sheinman, M., Schbath, S. and Arndt, P.F.
(2015).
How Evolution of Genomes Is Reflected in Exact DNA Sequence Match Statistics.
Molecular Biology and Evolution.
32 524-535.
[ DOI ]
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[43]
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De Paepe, M., Hutinet, G., Son, O., Amarir-Bouhram,
J., Schbath, S. and Petit, M.-A.
(2014).
Temperate phages acquire DNA from defective prophages by relaxed
homologous recombination: The role of Rad52-like recombinases.
PLOS Genetics.
10(3) e1004181.
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[42]
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Dumazert, J., Stephan, J.-Y., Petit, M.-A. and
Schbath, S.
(2013).
Assessing the enrichment significance of a possition weight matrix
(PWM) along a DNA sequence.
In Journees Ouvertes Biologie Informatique Mathematiques
(JOBIM), (C. Gaspin and e. Lindley, N., ed.), 25-34, Toulouse, France.
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Schbath, S., Martin, V., Zytnicki, M., Fayolle,
J., Loux, V. and Gibrat, J.-F.
(2012).
Mapping reads on a genomic sequence: an algorithmic overview and a
practical comparative analysis.
Journal of Computational Biology.
19 796-813.
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[40]
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Fayyaz, A., Launay, G., Schbath, S., Gibrat, J.-F.
and Rodolphe, F.
(2012).
Statistical significance of threading scores.
Journal of Computational Biology.
19 13-29.
[ DOI ]
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[39]
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Devillers, H. and Schbath, S.
(2012).
Separating significant matches from spurious matches in dna
sequences.
Journal of Computational Biology.
19 1-12.
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[38]
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Schbath, S.
(2011).
Statistiques de motifs.
Gazette des mathematiciens.
130 60-65.
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[37]
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Devillers, H., Chiapello, H., Schbath, S. and
El Karoui, M.
(2011).
Robustness assessment of whole bacterial genome segmentations.
Journal of Computational Biology.
18 1155-1165.
[ DOI ]
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[36]
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Stefanov, V., Robin, S. and Schbath, S.
(2011).
Occurrence of structured motifs in random sequences: Arbitrary number
of boxes.
Discrete Applied mathematics.
159 826-831.
[ DOI ]
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[35]
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Schbath, S. and Hoebeke, M.
(2011).
Advances in genomic sequence analysis and pattern discovery.
(L. Elnitski, O. Piontkivska, and L. Welch, ed.), chapter R'MES: a
tool to find motifs with a significantly unexpected frequency in biological
sequences.
Science, Engineering, and Biology Informatics, vol. 7. World
Scientific.
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[34]
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Touzain, F., Petit, M.-A., Schbath, S. and
El Karoui, M.
(2011).
DNA motifs that sculpt the bacterial chromosome.
Nature Reviews Microbiology.
9 15-26.
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Devillers, H., Chiapello, H., Schbath, S. and
El Karoui, M.
(2010).
Assessing the robustness of complete bacterial genome segmentations.
In RECOMB-CG 2010, (E. Tannier, ed.). Lecture Notes in
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6398 173-187.
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Reynaud-Bouret, P. and Schbath, S.
(2010).
Adaptive estimation for Hawkes' processes; Application to genome
analysis.
Annals of Statistics.
38 (5) 2781-2822.
[ DOI |
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[31]
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Schbath, S. and Robin, R.
(2009).
Scan Statistics - Methods and Applications.
(J. Glaz, I. Pozdnyakov, and S. Wallenstein, ed.), chapter How
pattern statistics can be useful for DNA motif discovery?
Statistics for Industry and Technology. Birkhauser.
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Schbath, S., Lacroix, V. and Sagot, M.-F.
(2009).
Assessing the exceptionality of coloured motifs in networks.
EURASIP Journal on Bioinformatics and Systems Biology.
ID 616234 1-9.
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[29]
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Mercier, R., Petit, M.-A., Schbath, S., Robin, S.,
El Karoui, M., Boccard, F. and Espeli, O.
(2008).
The MatP/matS site specific system organizes the Terminus region
of the E. coli chromosome into a Macrodomain.
Cell.
135 475-485.
[ DOI ]
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[28]
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Touyar, N., Schbath, S., Cellier, D. and Dauchel,
H.
(2008).
Poisson approximation for the number of repeats in a Markov chain
model.
J. Appl. Prob.
45 440-455.
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[27]
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Touzain, F., Schbath, S., Debled-Rennesson, I.,
Aigle, B., Leblond, P. and Kucherov, G.
(2008).
SIGffRid: a tool to search for σ factor binding sites in
bacterial genomes using comparative approach and biologically driven
statistics.
BMC Bioinformatics.
9:73 1-23.
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[26]
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Picard, F., Daudin, J.-J., Koskas, M., Schbath, S.
and Robin, S.
(2008).
Assessing the exceptionality of network motifs.
J. Comp. Biol.
15:1 1-20.
[ http |
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[25]
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Halpern, D., Chiapello, H., Schbath, S., Robin,
S., Hennequet-Antier, C., Gruss, A. and El Karoui, M.
(2007).
Identification of DNA motifs implicated in maintenance of bacterial
core genomes by predictive modelling.
PLoS Genetics.
3(9) e153.
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[24]
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Robin, S., Schbath, S. and Vandewalle, V.
(2007).
Statistical tests to compare motif count exceptionalities.
BMC Bioinformatics.
8:84 1-20.
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[23]
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Roquain, E. and Schbath, S.
(2007).
Improved compound Poisson approximation for the number of
occurrences of multiple words in a stationary Markov chain.
Adv. Appl. Prob.
39 1-13.
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[22]
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Stefanov, V., Robin, S. and Schbath, S.
(2007).
Waiting times for clumps of patterns and for structured motifs in
random sequences.
Discrete Applied Mathematics.
155 868-880.
[ DOI |
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[21]
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Matias, C., Schbath, S., Birmelé, E., Daudin,
J.-J. and Robin, S.
(2006).
Network motifs: mean and variance for the count.
REVSTAT.
4 31-51.
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Robin, S., Rodolphe, F. and Schbath, S.
(2005).
DNA, Words and Models.
Cambridge University Press, English version of ADN, mots et
modèles, BELIN 2003.
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Gusto, G. and Schbath, S.
(2005).
FADO: a statistical method to detect favored or avoided distances
between motif occurrences using the hawkes' model.
Statistical Applications in Genetics and Molecular Biology.
4 1.
Article 24.
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Reinert, G., Schbath, S. and Waterman, M.
(2005).
Applied Combinatorics on Words.
volume 105 of Encyclopedia of Mathematics and its
Applications, chapter Statistics on Words with Applications to Biological
Sequences.
Cambridge University Press.
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Schbath, S.
(2004).
A la recherche de mots de fréquence exceptionnelle dans les génomes.
In Images des Mathématiques, volume 3, 100-106. CNRS.
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Robin, S., Rodolphe, F. and Schbath, S.
(2003).
ADN, mots et modèles.
BELIN.
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[15]
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Schbath, S.
(2003).
Statistical methods in physical mapping.
In Encyclopedia of the Human Genome, number 434 in Mathematical
genetics. Nature Publishing Group.
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[14]
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Robin, S., Daudin, J.-J., Richard, H., Sagot,
M.-F. and Schbath, S.
(2002).
Occurrence probability of structured motifs in random sequences.
J. Comp. Biol.
9 761-773.
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Robin, S. and Schbath, S.
(2001).
Numerical comparison of several approximations of the word count
distribution in random sequences.
J. Comp. Biol.
8 349-359.
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[12]
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Reinert, G., Schbath, S. and Waterman, M.
(2000).
Probabilistic and statistical properties of words.
J. Comp. Biol.
7 1-46.
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Schbath, S., Bossard, N. and Tavaré, S.
(2000).
The effect of non-homogeneous clone length distribution on the
progress of an STS mapping project.
J. Comp. Biol.
7 47-58.
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Schbath, S.
(2000).
An overview on the distribution of word counts in Markov chains.
J. Comp. Biol.
7 193-201.
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El Karoui, M., Biaudet, V., Schbath, S. and Gruss,
A.
(1999).
Characteristics of Chi distribution on several bacterial genomes.
Research in Microbiology.
150 579-587.
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[8]
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Reinert, G. and Schbath, S.
(1999).
Compound Poisson approximations for occurrences of multiple words.
In Statistics in Genetics and Molecular Biology,
(F. Seillier, ed.). IMS Lecture Notes-Monograph Series.
Vol. 33.
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Schbath, S. and Bouvier, A.
(1998).
Finding words with unexpected frequencies in dna sequences.
In Explorapedia of Statistical and Mathematical Techniques for
use in Research and Technology.
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Reinert, G. and Schbath, S.
(1998).
Compound Poisson and Poisson process approximations for
occurrences of multiple words in markov chains.
J. Comp. Biol.
5 223-254.
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Schbath, S.
(1997b).
An efficient statistic to detect over- and under-represented words in
DNA sequences.
J. Comp. Biol.
4 189-192.
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Schbath, S.
(1997a).
Coverage processes in physical mapping by anchoring random clones.
J. Comp. Biol.
4 61-82.
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Schbath, S.
(1995a).
Compound Poisson approximation of word counts in DNA sequences.
ESAIM: Probability and Statistics.
1 1-16. (http://www.emath.fr/ps/).
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Schbath, S.
(1995b).
Étude asymptotique du nombre d'occurrences d'un mot dans une
chaîne de Markov et application à la recherche de mots de fréquence
exceptionnelle dans les séquences d'ADN.
PhD thesis, Université René Descartes, Paris V.
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Schbath, S., Prum, B. and Turckheim, E. d.
(1995).
Exceptional motifs in different Markov chain models for a
statistical analysis of DNA sequences.
J. Comp. Biol.
2 417-437.
[ .ps ]
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