The taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism.The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases  and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name. and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.The Taxonomic Search Engine is available at  Biological taxonomy provides the central link between diverse items of information about an organism. Given the scientific name of an organism, a researcher can query a wide range of databases for information on that organism's genome, development, morphology, geographic distribution, behaviour, phylogeny, and conservation status. However, the utility of taxonomic names as keys to accessing information is hampered by several factors, notably the lack of a single authoritative list of all taxonomic names ,2. In thdocumented API. Taxonomic names themselves have limitations as identifiers in databases [Morus applies to both an animal (the gannet) and a plant (the mulberry tree). Even species names can be identical – a species of wasp and a species of conifer both share the name Agathis montana. Hence, using names alone to link different data sources can be prone to error. As an example, at the time of writing NCBI's LinkOut feature [Loricaria (tax_id = 52085) to the TreeBASE [Loricaria (TaxonID = 1305), which is a plant genus (family Compositae).In the absence of a single database of names, one solution is to use a federated approach  where muatabases  due to t feature  mistakenTreeBASE  taxon LoTo avoid ambiguity some form of identifier other than a taxonomic name needs to be employed, such as Digital Object Identifiers (DOIs)  or Life Given the lack of a central list of names, and the limitations of names as identifiers, there is a clear need for a taxonomy name service that can validate names and provide unique identifiers . The SPIThis paper describes the Taxonomic Search Engine (TSE), which takes federated approach to the problem of searching for taxonomic names. Unlike the SPICE project, the TSE relies solely on the interfaces made available by the data source. A wrapper is created for each source database, but this resides on the same machine as the TSE. In this way, no special demands are made of the source database. The TSE searches multiple databases for a name, and returns the result in a consistent format. For each name, TSE also creates a LSID, so that each name from each source database has a globally unique identifier.The TSE uses five data providers: ITIS, Index Fungorum, IPNI, uBIO, and the NCBI.The Integrated Taxonomic Information System (ITIS)  was estade facto source of taxonomic data in biodiversity informatics projects.ITIS provides a classification of taxonomic names , and where more than one name exists for a taxon, ITIS specifies which name it regards as correct . Every name in the database, regardless of taxonomic status or position in the hierarchy is assigned a unique identifier . The database schema is fully documented, and the entire database is available for downloading by FTP as a SQL schema with the data in delimited text files. As a consequence, ITIS is frequently used as the The International Plant Names Index (IPNI)  combines which returns an XML document. If more than one name exists for a fungus, Index Fungorum designates one name as the "current name." It also reports the basionym (first recorded name) for that taxon. Index Fungorum does support a detailed hierarchical classification in the form of a lineage, but higher level taxa are not assigned records in the database . In fungal taxonomy, names are often assigned to the asexual state (anamorph) of a fungus for which the sexual state (telomorph) is unknown. Names for anamorphs are flagged as such in the database.IndexFungorum  is a dat which returns a nested array data structure.The Universal Biological Indexer and Organizer (uBio)  is a proThe NCBI Taxonomy database  is a curThe basic architecture of the TSE is summarised in Fig. IsAlive, NameSearch, and GetDataForID, which must be overridden in descendant classes. The IsAlive method queries whether the data source is available. The NameSearch method queries a data source for a given string. If one or more names are found, NameSearch returns basic information about that name, including the identifier used by the data source. This identifier is used by the GetDataForID method to query the data source for more details about the name.Each wrapper is derived from the same base class which provides some generic routines for creating XML documents and for caching results (see next section). The wrapper class supports three methods, In order to improve the responsiveness of the search engine, the results of queries to each source database are cached for 24 hours. The results of the query are stored in the format returned by the database , except for uBio where the SOAP response is serialised to disk.The Taxonomic Search Engine seeks exact matches to the user supplied query. In order to accommodate spelling mistakes the web interface to the search engine supports approximate string matching using two techniques. The first employs agrep  to searcThe TSE has a simple web interface Fig. . The use. The service provides two operations: NameSearch which queries the source databases for a user-supplied name, and SpellingSuggestion, which suggests alternative spellings for a name. Hence users can write web service clients that can use the TSE as part of their own applications. The TSE web site provides source code for two simple clients written in perl.The TSE has a SOAP web service that is described by a Web Services Description Language (WSDL) file available at Homo sapiens in the ITIS database would have the LSID:A LSID is a Uniform Resource Name (URN) comprising five parts: the Network Identifier ("lsid"), the root DNS name of the issuing authority, a namespace, an object identifier, and optionally a revision id to indicate the version . TSE genurn:lsid:itis.usda.gov.lsid.zoology.gla.ac.uk:tsn:180092Homo sapiens.where "tsn" is the "taxonomic serial number" used by ITIS as a unique identifier for each taxonomic name, and "180092" is the tsn for The TSE uses the perl library distributed by IBM's Life Science Identifier project  to creatThe 2004 edition of the Species 2000 CD-ROM  was usedTo create a test dataset, 1000 names were selected at random from the Species 2000 dataset. Each name was sent to the TSE web service by a perl script which recorded the time taken for each source database to respond to the query, and whether that source database contained the name. The time recorded is from the time the query was made until the time the response was returned – post processing by the TSE is not included in the measurement. For this experiment, the cache feature was turned off so that for each query the TSE went to the external source database, rather than using a local copy of the query result.The results of the simple performance benchmarks are shown in Table The TSE can be extended to handle additional data sources simply by deriving a new wrapper class from the base class. To date wrappers have only been written for data sources which can return plain text, XML, or SOAP messages. There are many more taxonomic databases that could be queried if wrappers were written to handle HTML output ("screen scraping"). However, this would make the wrapper very vulnerable to changes in web page design . Of courDespite the reasonable performance of TSE, there are obvious limitations in the current design and implementation. The PHP language does not support threads, so each source database is queried sequentially. As additional source databases are added the time to complete the search will get progressively longer. If the performance of additional databases is comparable to those already being queried Table , then eaSome source databases have broad taxonomic coverage such as ITIS, NCBI, and uBio, whereas others are restricted to particular groups, such as fungi (Index Fungorum) and vascular plants (IPNI). Hence, it makes little sense to query Index Fungorum or IPNI for an animal name . An option to select the databases to query could be easily added to the TSE web interface. However, it would be more efficient if the TSE could determine which databases were relevant to the user's query. If the TSE knew that the query string was the name of a fungus, it could send the query to the appropriate database. In practice, however, this is problematic. In order to know what organism a name refers to the TSE would have to have access to a databases of names and their classification – the very lack of such a database is the motivation behind the TSE in the first place. Furthermore, as discussed above, the same name can apply to different organisms. A user searching using the term "Morus" might be looking for a plant name, or an animal name (or perhaps both). There is some scope for more intelligent querying, such as looking for aspects of the name that are specific to one of the codes of nomenclature , but any such effort needs to be done with care – for example, "Compositae" is a family of plants.The Taxonomic Search Engine is a simple tool for querying multiple taxonomic databases. Typically, results of querying five major databases are returned in a few seconds. In addition to providing basic information about a name, the TSE acts as a LSID authority, providing globally unique identifiers for each name. The TSE provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names..The source code for the TSE, the web site, and the LSID authorities is available from the TSE site ), and a developer key from Google [TSE requires a web server and the PHP scripting language. It has been developed and tested under Red Hat Linux 8.0 with the Apache web server version 2.0.40 and PHP version 4.2.2, and Mac OS X 10.2.8 with Apache version 1.3.29 and PHP version 4.3.4. If PHP does not have the XSLT extension enabled then the user will either have to recompile PHP, or install the Sablotron toolkit . The codm Google .


Molecular motors harness the energy of ATP  and transform it into mechanical force. Well-known examples of motors include myosin and dynein, proteins that use ATP to ferry intracellular cargo along fibers made of actin or tubulin proteins. The ATP-dependent assembly of actin or tubulin fibers itself can work as a motor: for instance, the march of white blood cells toward pathogens is powered by the growth of actin filaments pushing against the cells' membranes. In all cases, coherent motion implies a coordinated and polarized use of energy.Now, Julia Cox, Oleg Tsodikov, and Michael Cox present evidence indicating that filaments of the bacterial RecA protein, long known for their role in homologous recombination and DNA repair, have properties reminiscent of a molecular motor as well. RecA filaments consist of DNA helices lined with RecA protein. RecA filaments invade a region of double-stranded DNA with similar nucleotide sequence, displacing one strand to pair with the other. Strand invasion can lead to a re-assortment—known as recombination—of DNA regions on either side of the shared sequence. It can also initiate the repair of DNA lesions during replication—the process by which a DNA molecule is copied to make two.RecA is also an ATPase, an enzyme capable of hydrolyzing (breaking down) ATP, when bound to DNA. RecA uses ATP to carry out strand exchange over long sequences and impose direction to the exchange, to bypass short sequence heterogeneities, and to stall replication so DNA lesions can be mended. But how RecA molecules within a filament coordinate and organize their activities to carry out these functions has remained obscure.Cox et al. addressed this problem in the test tube, by examining RecA filaments grown from mixing RecA protein with DNA. Previous experiments have shown that filament assembly spreads rapidly in the 5′-to-3′ direction once the first RecA molecule is loaded onto DNA. At the same time, ATP hydrolysis causes the release of RecA from DNA, but the exact rate of RecA dissociation is not known. Experiments suggest, however, that RecA molecules only dissociate from DNA when they are at the fiber's 5′ end, while ATP hydrolysis occurs all along its length.Under their experimental conditions, the authors found that a RecA molecule hydrolyzed 20 ATPs per minute—or one ATP every three seconds. If ATP hydrolysis occurred randomly in the fiber, one would expect a RecA molecule to dissociate about once every 1.5 seconds, or 40 RecA molecules to dissociate per minute. But this is not what the authors found—instead they estimated the dissociation to be at a rate of 120 RecA molecules per minute. Hence, six RecA molecules dissociate from a fiber in the time—three seconds—it takes for an individual RecA molecule to burn one ATP. This implies a filament organization in which every RecA molecule hydrolyzes ATP in synchrony with the sixth RecA molecule to its left and the sixth RecA molecule to its right.The authors note that there are approximately six RecA molecules per helical turn in a RecA filament. They propose that the RecA molecules hydrolyzing ATP at any given moment are aligned in a “stripe” that runs along the side of the filament. This stripe of ATP hydrolysis moves around the fiber in a repeating pattern of six steps. At the 5′ end of the fiber, ATP hydrolysis leads to RecA release. But in the middle of the fiber, it could work as a rotary motor, with the power to wind or unwind DNA and drive strand invasion through difficult passages of damaged DNA.


DNA polymerases α and δ play essential roles in the replication of chromosomal DNA in eukaryotic cells. DNA polymerase α (Pol α)-primase is required to prime synthesis of the leading strand and each Okazaki fragment on the lagging strand, whereas DNA polymerase δ (Pol δ) is required for the elongation stages of replication, a function it appears capable of performing on both leading and lagging strands, at least in the absence of DNA polymerase ε (Pol ε).in vivo and in vitro. Pol1 interacts with the C-terminal domain of Cdc27, at a site distinct from the previously identified binding sites for Cdc1 and PCNA. Comparative protein sequence analysis identifies a protein sequence motif, called the DNA polymerase interaction motif (DPIM), in Cdc27 orthologues from a wide variety of eukaryotic species, including mammals. Mutational analysis shows that the DPIM in fission yeast Cdc27 is not required for effective DNA replication, repair or checkpoint function.Here it is shown that the catalytic subunit of Pol α, Pol1, interacts with Cdc27, one of three non-catalytic subunits of fission yeast Pol δ, both The absence of any detectable phenotypic consequences arising from mutation of the DPIM suggests that despite its evolutionary conservation, the interaction between the two polymerases mediated by this motif is a non-essential one. Three conserved DNA polymerase enzymes whose activities are essential for complete chromosomal DNA replication have been identified through biochemical studies in mammalian systems  and combSchizosaccharomyces pombe, the Pol α-primase and Pol δ complexes are both heterotetrameric in structure [Each of the three essential polymerases is a multi-subunit entity, comprising a large catalytic subunit and a number of smaller subunits that are presumed to play either regulatory or structural roles . Little tructure . The cattructure . Both cotructure ,10, althtructure ,12, and tructure . Ortholotructure ,15.Perhaps surprisingly, there have been few reports of physical interactions between the various polymerase enzymes believed to be present at the eukaryotic replication fork. One exception to this comes from large-scale functional analysis of the budding yeast proteome where an interaction was uncovered between the catalytic subunit of the Pol α-primase complex, Pol1, and the C-subunit of the Pol δ complex in budding yeast, Pol32 . Interacin vitro, using purified recombinant proteins and peptides. Interaction is also seen between the human Cdc27 and Pol1 homologues, p66 and p180. The binding site for Pol1 maps to the extended C-terminal domain of the Cdc27 protein and requires the presence of a short protein sequence motif, which we have designated the DPIM, for DNA polymerase interaction motif. This short sequence is conserved in Cdc27 orthologues from a wide variety of eukaryotic species. Despite this evolutionary conservation, mutational inactivation of the Pol1 binding motif does not affect Cdc27 function in vivo. The implications of these results are discussed.In this paper, it is shown that the orthologues of the budding yeast Pol1 and Pol32 proteins in fission yeast, Pol1 and Cdc27 respectively, also interact in the two-hybrid system. It is also shown that these proteins are capable of interacting directly with one another, To test for interaction between fission yeast Pol1 and Cdc27, the two-hybrid system was used. Full-length Cdc27 fused to the activation domain (AD) of the yeast Gal4 protein was tested for its ability to interact with amino acids 278–527 of fission yeast Pol1 fused to the DNA binding domain of the bacterial transcription factor LexA . Amino acids 278–527 correspond to the smallest region of budding yeast Pol1 shown to interact with Pol32 . ReporteCip1-like PCNA binding sequence, the PIP box. To map the Pol1 binding site, a series of thirteen truncated Cdc27 proteins fused to the Gal4 AD were tested against LexA-Pol1 in the two-hybrid system. The results of this analysis are shown in Figure Previously, minimal Cdc1 and PCNA binding regions on Cdc27 have been mapped ,12,18. Cin vitro. Purified GST-Cdc27-273-352 fusion protein [In order to test whether the interaction between Pol1 and Cdc27 was a direct one, purified recombinant Cdc27 and Pol1 proteins were assayed for their ability to interact  protein  was testXenopus [DNA polymerase interaction motif (or DPIM) with the consensus sequence D(D/E)-G -- (V/I)(T/S). The sequences flanking the DPIM are generally highly charged in character and some sequence conservation (particularly of charged amino acids) is apparent in these regions. Mutagenesis and peptide binding studies (described below) suggest that, in addition to the DPIM, some of these sequences may also play a role in binding Pol1 (see Discussion).Previously, Cdc27 homologues from fission yeast ,18, buddXenopus  have beeXenopus  and ψ-BLXenopus ,24 were To examine whether the Pol1-Cdc27 (Pol1-Pol32) interaction observed in the yeasts was also conserved in higher eukaryotes, the catalytic subunit of human Pol α and the human Cdc27 orthologue p66/KIA00039 were assayed for interaction using the two-hybrid system. Amino acids 291–540 of the human Pol α catalytic subunit, corresponding to the minimum Cdc27 binding region (amino acids 278–527) in fission yeast Pol1, were expressed as a LexA fusion alongside Gal4 activation domain fusions of either the entire C-terminal domain of human p66 (amino acids 253–466) or the C-terminal 111 amino acids only (356–466). Interactions were tested by β-galactosidase assay. Both p66 constructs bound to Pol α Table , indicatin vivo by Cdc27. Similar reductions were seen with the Cdc27-P4 and Cdc27-P6 mutants, where the mutated residues are located N-terminal and C-terminal to the DPIM respectively. The conserved amino acids of the DPIM are therefore essential for Pol1 binding by Cdc27, although sequences flanking the conserved motif also play a role. Only three of the eight mutant proteins  were able to interact with LexA-Pol1 (278–527), though the strength of the interaction was reduced to 30–40% of the wild-type value for Cdc27-P3 and Cdc27-P5 , and to ~ 10% of wild-type for Cdc27-P7 . Immunoblotting showed that all the mutant proteins were present in yeast protein extracts at the same level as the wild-type Gal4-Cdc27 fusion protein (data not shown).Next, the importance for Pol1 binding of the conserved amino acids in the DPIM was examined. Eight mutant Cdc27 proteins  were constructed and tested as LexA fusions in the two-hybrid system . Removal of fifty amino acids from the N-terminus, or twenty amino acids from the C-terminus, of the Pol1 (278–527) protein was found to abolish the interaction with Cdc27 altogether Table .pol1 allele, pol1-ts13, had been reported [pol1+ by deletion of 9 bp from the ORF, resulting in loss of three amino acids  from within the minimal Cdc27 binding domain defined above. In this study, the ability of a Pol1-TS13(278–527) bait to bind to Cdc27 was tested using the two-hybrid system. No interaction could be detected at a range of growth temperatures . Indeed, no suppression was observed of any of three pol1 alleles that were analysed in this way, the others being pol1-1 [pol1-H4 [Previously, the isolation of a temperature-sensitive mutant reported . Sequenc3X-Cdc27  and trang pol1-1  and pol1in vivo role of the DPIM in Cdc27, four of the eight DPIM mutant alleles (cdc27-P1 through cdc27-P4) were cloned into plasmid pREP3X, 3' to the repressible nmt1 promoter [cdc27+/cdc27::his7+ diploid strain. Transformant colonies were then induced to sporulate and the spores plated on media containing thiamine, to repress the nmt1 promoter. Under these conditions, residual low level expression from the nmt1 promoter ensures that the level of Cdc27 protein present in the cell is comparable to that seen in wild-type cells [cdc27Δ haploid cells; indeed, no phenotypic defects were apparent (data not shown).To assay the promoter ,29, and pe cells . Analysicdc27+ gene was precisely replaced with the cdc27-Q1 mutant allele. In the Cdc27-Q1 mutant protein, the central five amino acids of the DPIM are replaced with alanine, resulting in loss of Cdc27-Pol1 interaction in the two-hybrid system . PCR analysis of genomic DNA using primers specific for wild-type cdc27+ and cdc27-Q1 sequences , with a generation time indistinguishable from wild-type (110 minutes at 32°C in YE medium). At 32°C, cdc27-Q1 cells underwent division at ~ 14.1 μm (compared to wild-type at ~ 14.4 μm). Further analysis showed that the cdc27-Q1 cells were indistinguishable from wild-type in all respects examined, including responses to the DNA replication inhibitor hydroxyurea (HU) and the DNA damaging agents methylmethane sulphonate (MMS), camptothecin (CPT), bleomycin sulphate (BMS) and UV light . The kinetics of cell division arrest in response to HU were also examined, but again no difference was detectable between cdc27-Q1 and wild-type strains (data not shown). That cell number increase is arrested in cdc27-Q1 cultures following treatment with HU, and that both wild-type and cdc27-Q1 cells become highly elongated under these conditions, is indicative of the presence of a functional DNA replication checkpoint in these cells. Taken together, these results strongly suggest that the Pol1-Cdc27 interaction does not play an essential role either during S-phase or in various DNA repair pathways in fission yeast.Phenotypic analysis of cdc27-Q1 background were also investigated. cdc27-Q1 was crossed to strains carrying mutations in the other three subunits of Pol δ  and in the helicase-endonuclease Dna2 and its associated protein Cdc24 (dna2-C2 and cdc24-M38). [cdc27-Q1 were indistinguishable from the single mutant. The cdc27-Q1 mutation was also combined with rad3Δ [cds1Δ [cdc27-Q1 rad3Δ and cdc27-Q1 cds1Δ double mutants, both of which were viable. The Rad3 and Cds1 proteins are key components of various DNA structure checkpoints in fission yeast [cdc27-Q1 double mutants were viable indicates that cdc27-Q1 cells do not require the presence of a functional checkpoint for viability. When wild-type cells are treated with hydroxyurea, activation of Cds1 results in cell cycle arrest and replication fork stabilisation. In the absence of Cds1, however, replication forks are believed to collapse, resulting in loss of viability [rad3Δ and cds1Δ double mutants were tested for their sensitivity to HU and CPT, but as before, no differences were observed between single and double mutants with cdc27-Q1 (data not shown).The consequences of introducing additional mutations into the 24-M38). ,30-33. Tth rad3Δ  and cds1on yeast . That thiability . The radIn fission yeast, DNA polymerase δ is a multisubunit complex comprising a large catalytic subunit that is required for chromosomal replication as well as three smaller subunits, two of which are also essential for cell viability ,18,36. Uin vivo and in vitro with Pol1, the catalytic subunit of Pol α. The Pol1 binding site on Cdc27 has been mapped by in vivo and in vitro approaches and a region of 40 amino acids, from amino acids 293 – 332, has been shown to be sufficient for binding. Protein sequence alignments of Cdc27 homologues across this 40 amino acid region identify a short protein sequence motif (D -- G --VT) that is highly conserved and which is essential for Pol1 binding. This DNA polymerase interaction motif (DPIM) is flanked by relatively highly charged sequences. Ten basic amino acids are found flanking the DPIM in the fission yeast Cdc27 protein sequence, nine of which are located N-terminal to the central conserved motif. Similarly, there are ten acidic amino acids, all of which lie either within the conserved DPIM or C-terminal to it. Our mutagenesis data clearly implicates several of these charged groups in the binding to Pol1 .The Cdc27 protein has an elongated shape, with a frictional ratio of 1.85 . The samRecently, the results of a deletion analysis of Pol32, the budding yeast orthologue of Cdc27, were reported , includiS. pombe cdc27-Q1 cells were indistinguishable from wild-type. No genetic interactions were observed between cdc27-Q1 and various other DNA replication or checkpoint mutants, including the key checkpoint kinase Rad3.In this paper, cells expressing the DPIM mutant protein Cdc27-Q1 were shown to be no more sensitive then wild-type to HU, MMS, CPT, UV and BMS (see Results), while analysis of budding yeast cells expressing Pol32 lacking the DPIM (270–309 deletion) showed them to be no more sensitive than wild-type to HU and UV, and to show normal rates of mutagenesis following UV exposure . Indeed,cdc27-Q1 mutation might be expected to be synthetically lethal or sick with a mutant that disrupted the overlapping redundant function. With the genetic tools available in yeast, this is a hypothesis that is readily testable.In conclusion, while the interaction between Pol α-primase and Pol δ mediated via Cdc27 could play an important role in coordinating the events of lagging strand synthesis, we have yet to obtain any evidence that this is the case. This raises two possibilities. First, that the observed interaction does not play an important role in chromosomal replication. We believe that this is unlikely to be the case, given the high degree of conservation of the DPIM sequence across evolution, in a region of the Cdc27 protein that is very poorly conserved at the primary sequence level. The second possibility is that there are multiple redundant interactions within the lagging strand machinery. In this case, an important role for the Cdc27-Pol1 interaction might only be uncovered when another protein-protein interaction is perturbed, in which case, the in vivo.In fission yeast, interaction between Pol α and Pol δ is mediated, at least in part, by direct binding of the Pol δ C-subunit Cdc27 to the Pol α catalytic subunit Pol1, and requires the presence of a short sequence motif (DPIM) in the C-terminal region of Cdc27. The DPIM is conserved in all known Cdc27 orthologues. Despite this, it has not been possible to identify any phenotypic consequences associated with deletion of the DPIM sequence, raising the possibility that the observed interaction does not play a crucial role pol1-H4 [pol1-1 [pol1-ts13 [polα-ts13 but is correctly renamed here to bring it in line with accepted nomenclature standards (see  for further details). S. pombe media and techniques were essentially as described [S. pombe was carried out by electroporation [S. cerevisiae CTY10-5d  was used [S. cerevisiae was cultured in YPDA and SD medium.All fission yeast strains were as described previously, except for pol1-H4  and pol1 [pol1-1 , which wol1-ts13 , which wescribed , with thporation , whereasporation . For twowas used . S. cereS. cerevisiae lexA op-lacZ strain CTY10-5d, as described previously [Two-hybrid interactions were monitored using the Gal4 transcription activation domain (prey) plasmid pACT2 (Clontech), the LexA DNA binding domain (bait) plasmid pBTM116, and eviously .pol1+ gene from S. pombe cosmid SPAC3H5, see , into plasmid pTZ19R) using oligonucleotides POL15 (5'-GTGTGGTTTGGGATCCCCCTATCACCAATGACACCTTTA-3') and POL13-2 (5'-GTGTGGTTTGGGATCCTACATCACCGTCATTGGAGGCGT-3'), restricting the PCR product with BamHI (sites underlined), cloning to pTZ19R (Fermentas) and sequencing to confirm the absence of errors. The 763 bp BamHI fragment was then transferred to pBTM116, to generate pBTM116-Pol1(278–527). Plasmid pBTM116-Pol1-TS13(278–527) was produced in a similar manner, except that the starting PCR template was genomic DNA prepared from pol1-ts13 cells [pol1+ ORF, resulting in the loss of coding capacity for amino acids L470, S471 and R472. Plasmids expressing C-terminally truncated Pol1(278–527) proteins were constructed by PCR amplification using oligo POL15 in conjunction with POL25-3 (5'-GTGTGGTTTGGGATCCTAAGGACCCATAACTCTTCTACT-3'), POL13-487 (5'-GTGTGGTTTGGGATCCTAAAAATTTGGTTGTTGTATTTT-3'), POL1-497 (5'-GTGTGGTTTGGGATCCTACCGGCACCAACTAGCATTTTT-3') or POL1-507 (5'-GTGTGGTTTGGGATCCTAGTTCTGAGGTGACGAACATCC-3'), cloning directly to pBTM116 following BamHI cleavage of the PCR product, and sequencing. The resulting constructs encoded the following proteins as Lex A fusions: Pol1(278–477), Pol1(278–487), Pol1(278–497) and Pol1(278–507). A construct expressing an N-terminally truncated Pol1(278-527) protein, designated Pol1(328–527), was generated by amplification using POL15-2 (5'-GTGTGGTTTGGGATCCCCGGCTCATTGTGTCTATTTGGC-3') with POL13-2 (above). Amplification with POL15-2 and POL25-3 generated a construct with the potential to express a LexA-Pol1 protein truncated at both ends: LexA-Pol1(328–477).The Pol1 bait plasmid pBTM116-Pol1-(278–527) was constructed by amplifying sequences encoding amino acids 278–527 from plasmid pTZ19R-Pol1 (prepared by subcloning a 5925 bp SmaI-PstI fragment encompassing the entire GGATCCCCTCAGAACAAGCAGTGAAAGAA-3') or P66-52 (5'-GTGTGGTTTGGGATCCCGTCTCCACCTCTTGAACCAGTG-3') with P66-3 (5'-GTGTGGTTTGGGATCCTTGGTCTTCACCCTTGACCACTC-3'). The PCR products were then restricted with BamHI, cloned to pACT2 and sequenced. The resulting plasmids encode, as Gal4 AD fusions, either the entire C-terminal domain of p66/KIAA0039  or a shorter region . Sequences encoding the interacting domain of the catalytic domain of human Pol α were amplified from plasmid pBR322-Pol α  using oligos HPOL1-5 (5'-GTGTGGTTTGGGATCCCCAAAGGGACCGTGTCCTACTTA-3') and HPOL1-3 (5'-GTGTGGTTTGGGATCCTACATCACGACAAGCGGTGGTGG-3'), restricted with BamHI, cloned to pBTM116 and sequenced. The resulting plasmid encodes amino acids 291–540 of the human protein as a LexA fusion.Sequences encoding the C-terminal region from human p66/KIAA0039 were amplified by PCR from plasmid pET19b-p66  using either oligo P66-51 (5'-GTGTGGTTTGGGATCCCCACCGAAGCAAAATCTGCTGCA-3') and PMUT3 (5'-GTTGTGGGTGGGATCCTACTTCTTAGTTGCAATGTTTAC-3'). Mutant Q1 was generated by PCR amplification with same primers but using pHBLA-Cdc27-Q1 (below) as template. Plasmids expressing mutants P5-P7 were generated by overlap extension PCR using PMUT5 and PMUT3 together with the following mutagenic oligonucleotides (shown with mutated sequence underlined in top strand oligo): CDC27-P5F (5'-AAAGAAAAGTTGCAGCGTACGCGACAACGAAAG-3'), CDC27-P5R (5'-CTTTCGTTGTCGCGTACGCTGCAACTTTTCTTT-3'), CDC27-P6F (5'-TTGGTTACTAAGGCAGCAGCAGTCTGGGAATCA-3'), CDC27-P6R (5'-TGATTCCCAGACTGCTGCTGCCTTAGTAACCAA-3'), CDC27-P7F (5'-GAATCATTTTCTGCAGCTGCAAACATCTCAACT-3'), CDC27-P7R (5'-AGTTGAGATGTTTGCAGCTGCAGAAAATGATTC-3').The Cdc27 prey plasmids were constructed in pACT2 (Clontech) and have been previously described, with the exception of mutants Cdc27-273-352-P1 through -P7, and Cdc27-273-352-Q1. The first four of these (P1-P4) were generated by PCR amplification from the pREP3X-Cdc27-P1 to -Cdc27-P4 plasmids described below. Oligonucleotides for PCR, with BamHI sites underlined: PMUT5 -encoding BamHI fragment described above was cloned into pQE32 (Qiagen). The resulting plasmid, pQE32-Pol1 (278–527), was transformed into E. coli M15 (pREP4) and recombinant protein expression induced in 500 ml cultures (OD600 nm of 0.6) by addition of IPTG to a final concentration of 1 mM. Following incubation for 4 hours at 37°C, the cells were pelleted and the pellet resuspended in 40 ml of ice-cold buffer A  containing EDTA-free Complete™ inhibitors (Roche Applied Science) and 1 mM PMSF. The cells were lysed by sonication, then centrifuged at 25000 g for 15 minute at 4°C. The soluble supernatant was added to 4 ml of 50% (v/v) Ni-NTA agarose (Qiagen) in buffer A, mixed on a wheel at 4°C for one hour, then packed into a disposable chromatography column at 4°C. The column was drained of the flow-through and subsequently washed with 100 ml of buffer B  containing Complete™ inhibitors and PMSF, then 100 ml of buffer A containing Complete™ inhibitors and PMSF, before the bound H6-Pol1 protein was eluted using 4 ml of buffer C . Following elution, samples were analysed by SDS-PAGE and the peak fractions pooled and dialysed overnight in PBS at 4°C. Protein concentration was determined by BCA assay (Pierce) versus BSA standards.The Pol1 (278–527) domain was expressed in GST and GST-Cdc27-273-352 fusion proteins were prepared as described previously  from plapol1+ locus in an otherwise wild-type leu1-32 ura4-D18 h- strain with sequences encoding thirteen copies of the 9E10 (c-myc) mAb epitope, such that the encoded Pol1 protein (termed Pol1-13Myc) carries the 9E10 epitopes at its C-terminus. Oligonucleotides for amplification from pFA6a-13Myc-kanMX6 [CGGATCCCCGGGTTAATTAA-3', with plasmid-specific sequence underlined) and POL1-13MYC-3 (5'-GGCAATTCCCAAGTCTTTGAAACAGGTATTCCCATCAACATTTCTTGTACTGCATGAGCAAATATCTGTTCGAGGTGTCGAATTCGAGCTCGTTTAAAC-3'). Following transformation of ~ 10 μg of PCR product, correct G418-resistant integrants were identified by PCR amplification of genomic DNA using primer FCGPOL1-5 (5'-ATGTCGTGGAAGCGTTCATT-3') within the pol1+ ORF and KAN269R (5'-GATCGCAGTGGTGAGTAACCATGCATCATC-3') within the kanMX6 cassette, and by Western blotting using the mouse anti-Myc mAb 9E10 (Roche).PCR-based gene targeting was used to tag the chromosomal c-kanMX6  were as 7 cells in mid-exponential growth in EMM medium. Cells were harvested, washed once in STOP buffer  and disrupted in buffer A  supplemented with Complete™ protease inhibitors (Roche), using a bead beater (Hybaid Ribolyser). Protein concentrations were determined at 595 nm using the BioRad protein assay reagent according to the manufacturer's instructions, with BSA as standard.Peptides were synthesized by Mimotopes . All peptides were synthesized to contain the sequence biotin-SGSG at the N-terminus. Peptide sequences: SpA AAPDEPQEIIKSVSGGKRRG; SpB PQEIIKSVSGGKRRGKRKVK; SpC KSVSGGKRRGKRKVKKYATT; SpD GKRRGKRKVKKYATTKDEEG: SpE KRKVKKYATTKDEEGFLVTK; HsA PKTEPEPPSVKSSSGENKRK; HsB EPPSVKSSSGENKRKRKRVL; HsC KSSSGENKRKRKRVLKSKTY; HsD ENKRKRKRVLKSKTYLDGEG; HsE RKRVLKSKTYLDGEGCIVTE. For binding assays, the peptides were initially bound to 10 μl Streptavidin-agarose beads in PBS in a final volume of 100 μl at room temperature for 1 hour with gentle agitation. After binding, the beads were washed 3 times with 100 μl PBS. Protein extracts (100 μg of fission yeast extract) were added to the beads, and incubated at 4°C for 1 hr with gentle agitation. The beads were pelleted by centrifugation at 2000 rpm for 3 minutes, and subsequently washed extensively with NP40 buffer  NP-40), prior to final resuspension with 20 μl of SDS loading buffer. Following SDS-PAGE, Pol1-13Myc was visualised by immunoblotting with the mouse anti-Myc mAb 9E10 (Roche). Fission yeast protein extracts were prepared from 8 × 10cdc27+ cDNAs  from pTZ19R-Cdc27-cDNA [GCCGCAGCAGGATTCTTGGTTACTAAGG-3'), CDC27-P2 (5'-CGACAACGAAAGATGAAGAAGCCGCCTTGGTTACTAAGGAAGAAG-3'), CDC27-P3 (5'-TCAAATCCGTATCCGGTGGAGCCGCAGCAGGGAAAAGAAAAGTTAAAAAG-3'), CDC27-P4 (5'-CCGGTGGAAAGAGACGTGGGGCCGCAGCAGTTAAAAAGTACGCGACAAC-3'). The resulting mutant alleles were tested for function by transforming a cdc27+/cdc27::his7+leu1-32/leu1-32 ura4-D18/ura4-D18 his7-366/his7-366 ade6-M210/ade6-M216 h-/h+ diploid [Plasmids pREP3X-Cdc27-P1 to -Cdc27-P4 were constructed by cloning mutagenised c27-cDNA  into pRE diploid , transfecdc27+ gene was first cloned into pTZ19R to make pHB-Cdc27. This vector was then modified by addition of the S. cerevisiae LEU2 gene and S. pombe ars1, to make pHBLA-Cdc27. The cdc27+ gene was then subjected to oligonucleotide-directed in vitro mutagenesis using the QuikChange method (Stratagene) with oligonucleotides CDC27-QC1  and CDC27-QC2 (5'-CTTCTTCCTTAGTAACCAAGgcggccgcTGCAGCTTTCGTTGTCGCGTAC-3'), to create plasmid pHBLA-Cdc27-Q1. This was then was transformed into a cdc27+/cdc27::ura4+ diploid, and cdc27::ura4+ (pHBLA-Cdc27-Q1) haploids obtained following sporulation and regrowth. These were then plated on YE plates containing 1 mg/ml 5-FOA. 5-FOA resistant colonies were identified, purified and characterised by PCR amplification of genomic DNA ; CDC27-Q1M-DIAG2 ; CDC27-H (5'-ACTGGTAGAATTGCGTTCGCGCTC-3'); CDC27-B (5'-TCTAGGATCAGAGTGAACTGATTG-3'); CDC27-SEQ2005 (5'-AGGTTGTACTAACATTAACAG-3'). The resulting strain, cdc27-Q1 leu1-32 ura4-D18 his7-366 ade6-M216 h- was then analysed alongside the wild-type leu1-32 ura4-D18 his7-366 ade6-M216 h-, as described below.A 3.1 kb HindIII-BamHI genomic DNA fragment carrying the oworkers ), to ens6 cells/ml) in YE medium and ~ 2000 cells plated on YE medium supplemented with varying concentrations of hydroxyurea , methylmethanesulphonate , bleomycin sulphate , or camptothecin . Cells were also plated on plates containing sub-lethal doses of both HU and CPT, specifically 7.5 mM HU with either 5.5 or 6 μM CPT, or 10 mM HU with either 5.5 or 6 μM CPT. To analyse the effects of HU treatment in liquid culture, HU was added to EMM medium to a final concentration of 12 mM. Cell number per ml of culture was monitored using a Coulter Z1 electronic particle counterTo test sensitivity to UV, 1000 cells were plated on EMM plates, allowed to dry for 20 minutes, then irradiated using either a Stratalinker UV source (Stratagene) over the range 0 – 250 J/m2. Following UV treatment, plates were placed immediately in the dark to avoid photoreversal. For all treatments, the efficiency of colony formation was determined after 4 days growth at 32°C. Growth rate  was determined by cell counting using a particle counter. Cell length at cell division was determined using a graduated eyepiece.Cells were grown to mid-exponential phase ; PCNA (proliferating cell nuclear antigen).In Edinburgh, SM conceived of the study, performed some of the experimental work and prepared and revised the final manuscript, while FG performed the remainder of the experimental work. In Dundee, EW and JRGP designed and carried out the peptide binding studies. All four authors read and approved the manuscript.


Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes , and the selection of the number of segments in the profile.We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context.Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome. Chromosomal aberrations often occur in solid tumors: tumor suppressor genes may be inactivated by physical deletion, and oncogenes activated via duplication in the genome. Gene dosage effect has become particularly important in the understanding of human solid tumor genesis and progression, and has also been associated with other diseases such as mental retardation ,2. Chrom2 of fluorescence ratios are ranked and plotted according to the physical position of their corresponding BACs on the genome [The purpose of array-based Comparative Genomic Hybridization (array CGH) is to detect and map chromosomal aberrations, on a genomic scale, in a single experiment. Since chromosomal copy numbers can not be measured directly, two samples of genomic DNA (referred to as the reference and test DNAs) are differentially labelled with fluorescent dyes and competitively hybridized to known mapped sequences (referred to as BACs) that are immobilized on a slide. Subsequently, the ratio of the intensities of the two fluorochromes is computed and a CGH profile is constituted for each chromosome when the loge genome . Differee genome ,8.2(ratio) = 0 for regions of no change, segments with positive means represent duplicated regions in the test sample genome, and segments with negative means represent deleted regions. Even if the underlying biological process is discrete (counting of relative copy numbers of DNA sequences), the signal under study is viewed as being continuous, because the quantification is based on fluorescence measurements, and because the possible values for chromosomal copy numbers in the test sample may vary considerably, especially in the case of clinical tumor samples that present mixtures of tissues of different natures.Each profile can be viewed as a succession of "segments" that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. Array CGH data are normalized with a median set to logposterior empirical criterion. These methods are defined by a criterion to optimize and an algorithm of optimization. Different criteria have been proposed: the likelihood criterion [Two main statistical approches have been considered for the analysis of array CGH data. The first has focused many attentions, and is based on segmentation methods where the purpose is to locate segments of biological interest ,9-11. A riterion , partialriterion , and algriterion , binary riterion ) and adaad hoc procedures [.As for the estimation of the number of segments, the existing articles have not defined any statistical criterion adapted to the case of process segmentation. This problem is theoretically complex, and has lead to ocedures -11. Sincocedures . We prop. This example shows the practical consequences of the use of theoretically unappropriated criteria. This point constitutes the main purpose of the discussion (see the Discussion Section).To show the importance of the choice of the model selection criterion on simple data, we use the results of a single experiment performed on fibroblast cell lines , with one known chromosomal aberration. Figure K* = 5. In the first situation, the segments are regularly spaced with a jump of the mean of 1  that could be interesting to detect in the case of array CGH profile (a putative gained region for instance). Since the previously described criteria [The behavior of the criterion we propose is different. It seems to be more robust to the noise, as it will give a number of segments that is close to the true number. In particular, the irregular configuration presents a segment of small size . The effect of additional noise is to widden the zone of estimation, but the estimated break-points remain close to the true break-points. If the true break-point is located at t*, the estimated break-point stays in the interval t* ± 3. In the irregular configuration, additional noise has similar effects on the break-point's positioning, but the probability to correctly estimate a break-point depends on the jump of the mean between two segments. In the irregular case, Figure t = 40 the difference of mean is d = 2, and the probability to locate the break-point at the true coordinate is higher than 0.65 for any additional noise. On the contrary, at position t = 85 where the different of mean equals d = 0.5 the probability to correctly locate the break-point decreases dramatically with the noise (probability 1 for σ = 0.1 and probability 0.25 for σ = 0.5). This means that dynamic programming is sensitive to small segments that present little differences in the mean regarding the noise. Nevertheless, the example on the real data set presented in Figure The second simulation-based result concerns the ability of dynamic programming to locate the break-points at the correct coordinate, given different amounts of noise Figures  and 4. I where each segment has a specific mean and variance [, where the variance is common between segments [The CGH profile is supposed to be a Gaussian signal. In a segmentation framework, two types of changes can be considered: changes in the mean and the variance of the signal, or changes in the mean only. Let us define model variance , and modsegments . and  when our criterion is used to estimate the number of segments. The main result of this comparison is that the number of segments is higher using model  compared to model . This behavior of model  could be interpreted as a trend to divide large segments into smaller parts, in order to maintain the variance homogeneous between segments. This leads to a more segmented profile, maybe more precise, but that may be more difficult to interpret in terms of relative copy numbers. Nevertheless, as model  allows the exploration of segments with one observation, it will be more efficient for the identification of outliers, as shown in Figure , point at 100 Mb).Since both models can be used, it is important to explore their behavior in order to know which model is the best adapted to the special case of array CGH data. We use clinical data obtained from primary dissected tumors of colorectal cancers . Figure The definition of an appropriate penalized criterion has been an issue for previous works using segmentation methods for array CGH data analysis ,9,11. In gives the best segmentation with K segments (see the Methods Section). In real situations this number is unknown, and one has to choose among many possible segmentations. The maximum of the log-likelihood  can be viewed as a quality measurement of the fit to the data of the model with K segments, and will be maximal when each data point is in its own segment. Therefore selecting the number of segments only based on the likelihood criterion would lead to overfitting. Furthermore, the number of parameters to estimate is proportional to the number of segments, and a too large number of segments would lead to a large estimation error. A penalized version of the likelihood is used as a trade-off between a good adjustement and a reasonable number of parameters to estimate. It is notedWhen the number of segments is known, the maximization of the log-likelihood pen(K) is a penalty function that increases with the number of segments, and β is a constant of penalization. The estimated number of segments is such as :where K-dimensional, ie for a fixed K the criterion has to be globally maximized to ensure convergence of the break-point estimators to the true break-points [It is crucial to notice that the criterion which is penalized should provide the best partition of k-points . This opk-points ,10.Classical penalized likelihoods use the number of independent continuous parameters to be estimated as a penalty function. Even though those criteria are widely used in the context of model selection, theoretical considerations suggest that they are not appropriate in the context of an exhaustive search for abrupt changes.K (K means and K variances) for a heteroscedastic model with K segments. Penalized criteria have already been used in the context of array CGH data analysis to estimate the number of segments [K parameters, they implicitly consider that the break-points are also continuous parameters, leading to a new penalty function pen(K) = 3K - 1, which considers K - 1 break-points. Nevertheless, the characteristic of break-point detection models lies in the mixture of continuous parameters and discrete parameters that can not be counted as continuous parameters, since the number of possible configurations for K segments is finite and equals   [Let us focus on the penalty function in a first step. Table segments . In addi points) . that takes the complexity of the visited configurations into account. It is written pen(K) = 2K(c1 + c2), where c1 and c2 are constant coefficients that have to be calibrated using numerical simulations. Since AIC and BIC and the criterion proposed in [β. This term is constant in the case of AIC and BIC , and contributes to the oversegmentation as mentioned above. This can lead to an empirical choice for the constant, in order to obtain expected results based on a priori knowledge. For this reason, an arbitrary penalty constant can be chosen for the procedure to select a reasonable number of segments (β = 10/3 in [β can be adaptively chosen to the data [pen(K) = 2K and to define an automatic procedure to choose the constant of penalization β adaptively. We explain the estimation procedure for the penalty constant in the Methods Section.This leads to the definition of a new penalty function adapted to the special context of the exhaustive search of abrupt changes. This function table  is propoposed in  do not c 10/3 in ). Insteathe data ,14. FurtThe power of adaptive methods for model selection lies in the definition of a penalty that is not universal (such as in the case of AIC and BIC). This means that the dimension of the model is estimated adaptively to the data. The efficiency of such method has been shown on simulated data as well as on experimental results (Results Section), and adaptive model selection criteria seem to be very appropriate for array CGH data analysis. and  concerns the modeling of the variance: model  assumes that the variability of the signal is organized along the chromosome, whereas model  specifies that the variance is constant. Since it has been shown that the vast majority of clones all had the same response to copy number changes in the aneuploid cell lines [ would be justified regarding this experimental argument.Since the choice of modeling affects the resulting segmentation, it is crucial to provide guidelines for their use. This can be done with the interpretation of the statistical models in terms of their biological meaning. The difference between model ll lines , the use, that can detect changes for one data point, whereas with model  outliers would belong to segments with higher variance.Outliers seem to be a major concern in microarray CGH data analysis. For instance, if only one BAC is altered whereas its neighbors are not, the conclusion could be either that it is biologically relevant, or that the signal is due to technical artefacts. Replications are crucial in this situation, as well as secondary validations. An other possibility could be that the BAC is misannotated: if the ratio is plotted at the wrong coordinate on the genome, it will appear as an outlier, when it is not. The importance of outlier identification is another argument in favor of model It has to be noted that classical models used in segmentation methods assume the independence of the data. This may be a reasonnable assumption for BAC arrays whose genome representation is approximately 1 BAC every 1.4 Mb . NeverthMicroarray CGH currently constitutes the most powerful method to detect gain or loss of genetic material on a genomic scale. To date, applications have been mainly restricted to cancer research, but the emerging potentialities of this technique have also been applied to the study of congenital and acquired diseases. As expression profile experiments require careful statistical analysis before any biological expertise, CGH microarray experiments will require specific statistical tools to handle experimental variability, and to consider the specificity of the the studied biological phenomena. We introduced a statistical method for the analysis of CGH microarray data that models the abrupt changes in the relative copy number ratio between a test DNA and a reference DNA. We discuss the effects of different modelings that can be used in segmentation methods, and suggest the use of a model that considers the homogeneity of the signal variability based on experimental arguments and regarding the specificity of array CGH data.The main theoretical issue of array CGH data analysis lies in the estimation of the number of segments that requires the definition of appropriate penalty function and constant. We define a new procedure that estimates the number of segments adaptively to the data. This method selects the number of segments with high accuracy compared to previously mapped aberrations, and seems to be more efficient compared to others proposed to date. The use of dynamic programming remains central to localizing the break-points, and the simulation results show that when the good number of segments are selected, the algorithm localizes the break-points very close to the truth. Assessing the number of segments in a model is theoretically complex, and requires the definition of a precise model of inference. To that extent, microarray CGH analysis not only requires computational approaches, but also a careful statistical methodology.We briefly present the data we used in this article. The first data we use in the Results Section consist of a single experiment on fibroblast cell lines (Coriell Cell lines) whose chromosomal aberrations have been previously mapped. Those defaults concern partial or whole chromosome aneuploidy. This data have been previously used by other authors . The sec and . Let us consider a CGH profile, and note yt, the log2-ratio of the intensities for the tth BAC on the genome. Precisely yt represents the average signal obtained from the replicated spots on the slide. BACs are the basic units in our model, and are ordered according to their physical position. We suppose that the yt are the realizations of independent random variables {Yt}t = 1...n, with Gaussian distributions . We assume that K - 1 changes affect the parameters of the distribution of the Ys, at unknown coordinates  with convention t0 = 1 and tK = n, and that the parameters of the Ys distributions are constant between two changes:In this section, we define the models μk is the mean of the kth segment. Model  specifies that the variance is segment-specific , whereas  considers that the variance is common between segments (σ2). Since BACs are supposed to be independent, the log-likelihood can be decomposed into a sum of "local" likelihoods, calculated on each segments: , withwhere K and the segments' coordinates , we estimate the mean and the variance for each segment using maximum likelihood :Given the number of segments If the variance of the segments is homogeneous, its estimator is given by:K and . We will proceed in two steps: in the first step, we will consider that the number of segments is known, and the problem will be to estimate the tks, that is, to find the best partition of a set of n individuals into K segments. In the second step, we will estimate the number of segments, using a penalized version of the likelihood.Notice that when the segment coordinates are known, the estimation of the mean and variance for each segment is straightforward. Then, the key problem is to estimate K is known, the problem is to find the best partition of {1,...,n} into K segments, according to the likelihood, where n is the size of the sample. An exhaustive search becomes impossible for large K since the number of partitions of a set with n elements into K segments is . To reduce the computational load, we use a dynamic programming approach (programs are coded in MATLAB language and are available upon request). Let  be the maximum log-likelihood obtained by the best partition of the data {Y(i), Y(i + 1),...,Y(j)} into k + 1 segments, with k break-points, and let note . The algorithm is as follows:When the number of segments k + 1 segments is a union of a partition into k segments and a set containing 1 segment. This approach presents two main advantages: it provides an exact solution for the global optimum of the likelihood [(nK) to (n2) for a given K . At the end of the procedure, the quantities  are stored and will be used in the next step. Notice that this problem of partitioning is analogous to the search for the shortest path to travel from one point to another, where  represents the total length of a (k + 1)-step-path connecting the point with coordinate 1 to the point with coordinate n.Dynamic programming takes advantage of the additivity of the log-likelihood described above, considering that a partition of the data into kelihood , and red measures the adjustment of a model with K segments to the data, we aim at selecting the dimension for which  ceases to increase significantly. For this purpose, let us define a decreasing sequence (β) such as β0 = ∞ andThe purpose of this section is to explain an adaptive method to estimate the number of segments. Further theoretical developments can be found in . If we cpen(K), ), the sequence of βi represents the slopes between points (pen(Ki + 1), ) and (pen(Ki), ), where the subset {(pen(Ki),),i ≥ 1}) is the convex hull of the set {(pen(K),)}.If we represent the curve  of the likelihood:Since we aim at selecting the dimension for which K such that the second derivative is lower than a given threshold :and we select the highest number of segments s is arbitrary. According to our experience, a threshold s = -0.5 seems appropriate for our purpose. A criticism that can be made to this procedure is its dependency on the threshold which is chosen. Nevertheless, it is important to point out that despite this thresholding the procedure remains adaptive, since the penalty constant is estimated according to the data.Other procedures have been developed to automatically locate the break in the slope of the likelihood. Nevertheless, the criterion we use can be interpreted geometrically and is easy to implement. The choice of the constant K* = 5 segments. In the first case Figure d = 1. In the second case .We performe numerical simulations to assess the sensitivity of our procedure to the addition of noise. In the first case, we simulate 100 points with e Figure  the segmFP developed the statistical models and the programs dedicated to array CGH data analysis, ML developped the adaptive selection of the number of segments. SR, CV and JJD supervised the study.


Smoking habits do not seem to be the main explanation of the epidemiological characteristics of female lung cancer mortality in Asian countries. However, Asian countries are often excluded from studies of geographical differences in trends for lung cancer mortality. We thus examined lung cancer trends from 1971 to 1995 among men and women for 23 countries, including four in Asia.International and national data were used to analyze lung cancer mortality from 1971 to 1995 in both sexes. Age-standardized mortality rates (ASMR) were analyzed in five consecutive five-year periods and for each five-year age group in the age range 30 to 79. The age-period-cohort (APC) model was used to estimate the period effect (adjusted for age and cohort effects) for mortality from lung cancer.The sex ratio of the ASMR for lung cancer was lower in Asian countries, while the sex ratio of smoking prevalence was higher in Asian countries. The mean values of the sex ratio of the ASMR from lung cancer in Taiwan, Hong Kong, Singapore, and Japan for the five 5-year period were 2.10, 2.39, 3.07, and 3.55, respectively. These values not only remained quite constant over each five-year period, but were also lower than seen in the western countries. The period effect, for lung cancer mortality as derived for the 23 countries from the APC model, could be classified into seven patterns.Period effects for both men and women in 23 countries, as derived using the APC model, could be classified into seven patterns. Four Asian countries have a relatively low sex ratio in lung cancer mortality and a relatively high sex ratio in smoking prevalence. Factors other than smoking might be important, especially for women in Asian countries. Worldwide, over one million people die of lung cancer each year . In the Epidemiological studies have shown that cigarette smoking is the major cause of lung cancer in both sexes -8. HowevLong-term geographical trends in cancer mortality can provide useful information to assist etiological research. However, Asian countries are often excluded from studies of geographical differences in trends in lung cancer mortality. In order to clarify the changing patterns of lung cancer mortality worldwide, we examined lung cancer trends from 1971 to 1995 among men and women for 23 countries including four from Asia – Taiwan, Japan, Singapore, and Hong Kong. In addition, we plotted the pattern of mortality rate in these countries by using the age-period-cohort (APC) model.We used data from the World Health Organization (WHO) and Taiwan to analyze secular trends from 1971–1995 in lung cancer mortality in both men and women. Mortality data provided by WHO were relatively incomplete in some countries, so we analyzed data from 22 countries and Taiwan. The twenty-two countries – Hong Kong, Singapore, Japan, Portugal, Poland, Italy, Cuba, Spain, Hungary, France, Greece, Finland, United States, England and Wales, Netherlands, Belgium, Canada, Australia, New Zealand, Denmark, Norway and Sweden – are members of WHO. The data for Taiwan came directly from the Office of Statistics, Department of Health in Taiwan. Since rates for the under 30-year age group are often based on few deaths, and rates for the over 80-year group might be affected by competitive death effects, only rates for the age range 30 to 79 were considered, so as to ensure adequate reliability of the estimates. Lung cancer mortality rates between 1971 and 1995 were analyzed in five consecutive five year periods  and in five year age groups.1991–1995 - ASMR1971–1975) / (ASMR1971–1975)] × 100.Age-standardized mortality rates (ASMR) were calculated using the world population for 1976 as the reference . PercentIn order to apply the APC model, the matrix of age-specific death rates was calculated for each 5-year calendar period (from 1971–1975 to 1991–1995) and age group (from 30–34 to 75–79). The effect of period of death in the APC model was evaluated by a log-linear Poisson model with a modified method as described by Osmond and Gardner . BrieflyAge-standardized mortality rates from lung cancer per 100 000 population per year in 23 countries for 1971 to 1995 are listed for men in Table Table The mean values of the sex ratio of the ASMR from lung cancer in Taiwan, Hong Kong, Cuba, Singapore, and Japan were 2.10, 2.39, 2.78, 3.07, and 3.55, respectively, with a range of 0.17 to 0.51 over the five-year periods. These values were not only relatively constant over time, but were also lower than seen in the western countries. For example, the lowest sex ratio of 2.10 was seen in Taiwan, with the sex ratio remaining a relatively constant value over the entire period.Only age is adjusted when the ASMR is calculated. However, both age and cohort effect are adjusted in the APC model. The period effects for males and females from the APC model applied to the data from the 23 countries could be classified into seven patterns: 1) an increasing trend in both sexes, seen in Taiwan  declare that they have no competing interests.YPL was responsible for the development of intellectual content and the study design, collected and analyzed the data, interpretation of the results, manuscript drafting and the critical revisions of manuscript. YCH was responsible for the development of intellectual content, interpretation of the results and manuscript drafting. GWL was responsible for data coding and entry and statistical analyses.The pre-publication history for this paper can be accessed here: