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Genomics 55, 78–87 (1999)Article ID geno.1998.5631, available online at http://www.idealibrary.com on

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Integrated Mapping Package—A Physical MappingSoftware Tool Kit

Peisen Zhang,1 Xiaolu Ye, Li Liao, James J. Russo, and Stuart G. Fischer

Columbia Genome Center, Columbia University, New York, New York 10032

Received August 12, 1998; accepted October 13, 1998

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We have developed an integrated physical mappingomputer software package (IMP), originally designedo support the physical mapping of human chromo-ome 13 and expanded to support several gene-identi-cation projects based on the positional candidate ap-roach. IMP displays map data in a form that providesseful guidelines to the end users. An integrated mapith high resolution and confidence is constructed

rom different types of mapping data, including hy-ridization experiments, STS-based PCR assays, ge-etic linkage mapping, cDNA localization, and FISHata. The map is also designed to provide suggestionsor specific experiments that are required to obtain

aps with even higher resolution and confidence. Tohis end, the optimization employs multiple con-traints that take into account already establishedTS “scaffold” maps. This software thus serves as an

mportant general tool kit for physical mapping, se-uencing, and gene-hunting projects. © 1999 Academic Press

INTRODUCTION

In recent years, the “positional candidate” strategyas dominated the search for important disease genesCollins, 1995). Typically, there are multiple stages touch an endeavor, including genetic linkage, physicalapping, cDNA recovery strategies, screening of re-

ional ESTs, and eventually gene characterization andutation analysis. Multiple approaches exist for each

f these processes, and in many gene-hunting projects,everal or all of these approaches are used.For example, physical mapping may consist of re-

triction fingerprinting (Coulson et al., 1986; Olson etl., 1986; Kohara et al., 1987; Carrano et al., 1989;tallings et al., 1990; Trask et al., 1992), PCR-basedTS content mapping (Green and Olson, 1990; Foote etl., 1992; Chumakov et al., 1992), and clone-to-cloneybridization (Fischer et al., 1994) and often incorpo-ates maps and mapping reagents from external sources.

1 To whom correspondence should be addressed at the Columbiaenome Center, College of Physicians & Surgeons, 630 West 168thtreet, New York, NY 10032. Telephone: (212) 304-7555. Fax: (212)04-5515.

78888-7543/99 $30.00opyright © 1999 by Academic Pressll rights of reproduction in any form reserved.

imilarly, identification of candidate genes can includeuch methods as cDNA selection, exon trapping, use ofublic databases of regionally mapped genes or ESTs,omology searches (Altschul et al., 1990) based on sampleequencing, and the use of exon-prediction programsuch as GRAIL (Xu et al., 1994), Genefinder (Solovyev etl., 1994), and GeneScan (Burge and Karlin, 1997).We present here an expanded version of our mapping

rograms (Zhang et al., 1994) now assembled into anntegrated mapping package (IMP), which has the capa-ility of incorporating data from several mapping studiessing different approaches. A unique feature of the pack-ge is the option for the user to adjust scoring parametersepending on which data are considered the mostikely to be correct. For instance, genetic linkage oradiation hybrid data may form the boundary condi-ions within which more error-prone approaches suchs matrix hybridization of end-labeled clones must beontained. Thus, initial runs of the mapping algo-ithms may precede further optimization runs. Theata can be imported into a relational database man-gement system such as SYBASE or ORACLE. Finally,iven the dynamic nature of the genomics field, theMP has been designed to be flexible enough to incor-orate newer techniques and data (functional genom-cs databases, structural and motifs-based databases,tc.) as the need arises in a given project. We providehree example projects, undertaken at Columbia, inhich the IMP has been instrumental: (i) the genera-

ion of a high resolution YAC-cosmid map over most ofhe length of human chromosome 13 (Cayanis et al.,998), (ii) the development of two highly annotatedaps in the region surrounding BRCA2 (13q12.2; Fi-

cher et al., 1996), and (iii) the CLL deletion locus13q14.3; Kalachikov et al., 1997). This software devel-ped on the UNIX platform is available by contactinghe first author. A web page to use the software can beound at the following URL: http://genome1.ccc.olumbia.edu/;genome/IMAP/imap.html.

DATA, MAPS, AND METHODS

We developed an efficient methodology to assemblerdered cosmid contigs aligned to YACs, which integrates

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xperimental methods and computer technology, andave used this general approach to construct high-reso-

ution physical maps of human chromosome 13 (Fischert al., 1996). In support of this work, we developed theMP software. The details of the biological experimentalethod and contig assembly program have previously

een published (Zhang et al., 1994). To assemble physicalaps, the inter-Alu PCR probes of YACs are hybridized

o arrayed cosmids from the Los Alamos chromosome 13osmid library gridded on filters at high density. Contigsre assembled from the subsets of cosmids hybridized byhe YAC probes. Riboprobes from each cosmid are hybrid-zed to filters containing this subset of cosmids (Fisher etl., 1994). The assembly software, Cmap, previously de-cribed (Zhang et al., 1994), is used to generate cosmidontigs aligned to overlapping YACs. The maps gener-ted by IMP consist of two panels. The cosmid hybridiza-ion matrix forms the base; the map of cDNAs, markers,nd YACs overlying these cosmids form the upper panel.

ata Formats and Maps

The IMP has functions to generate different mapsccording to the data sets provided and the users’eeds. Cmap generates an ordered cosmid contig mapsing cosmid-to-cosmid riboprobe matrix hybridizationata (from the hybridization of riboprobes from thends of cosmid inserts to high-density cosmid colonylter membranes). Ymap generates a YAC-cosmid mapased on hybridization of YAC inter-Alu probes to cos-ids. Imap produces an integrated map based on var-

ous sets of mapping data, such as cosmid-to-cosmidiboprobe matrix hybridization data, YAC cosmid in-er-Alu hybridization data, preordered and floating se-uence-tagged-site (STS) data, cDNA data, geneticarker data, and radiation hybrid data. For these in-

egrated maps, the cosmid-to-cosmid data and theAC-to-cosmid data are essential. Other data sets areptional. With a very simple model, we explain belownput data formats and output maps. To distinguishifferent input data types, we adopted some namingonventions for the input files. While we use YACs andosmids in the descriptions here, the user can substi-ute PAC, BAC, P1, phage, and even plasmid clones.he important caveat is that what is substituted for

FIG. 1. Input data format (lef

he YAC should on average be substantially longerhan what is substituted for the cosmid.

1. Cosmid matrix hybridization data and cosmid con-igs. An input file of cosmid matrix hybridization datas created with a text editor (such as emacs) using theollowing format:

input_file:5[input_sentence. . .] ENDwhere “:5” means definition, “. . .” means the last

nit can be repeated as often as needed, “[ ]” meanshat choosing one or more of the enclosed items isptional, and END is necessary to terminate the input-file;input_sentence:5 cosmid [cosmids. . .] endwhere the word “end” is necessary to complete the

entence, cosmid is represented by an alphanumerictring, and the first cosmid in the string is by definitionhe probe.

For example, in Fig. 1, Cmap, using a very simpleodel data file, yields five ordered contigs and a sin-

leton C10. Note that C10 is not attached to the contigontaining C11 and C12 because of the absence ofwo-way hybridization results.

2. YAC cosmid inter-Alu hybridization data and cos-id-to-YAC maps. Create an input data file with the

ollowing format:

input_file:5[input_sentence. . .] ENDinput_sentence:5 YAC [cosmids. . .] endwhere “YAC” is represented by an alphanumeric

tring, and other notations are defined as before. Ourodel YAC-cosmid inter-Alu input data are shown inig. 2.

By running Ymap, the output map (Fig. 3) is ob-ained.

nd output matrix maps (right).

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3. Basic integrated map. By combining cosmid-ma-rix hybridization data with YAC-cosmid inter-Alu hy-ridization data, a supercontig can be assembled and aasic integrated map (as in Fig. 4) can be generated,ithout extra mapping information, as those presented

n a comprehensive integrated map (as in Fig. 3). Aupercontig refers to a set of cosmids, YACs, markers,DNAs, and other segments of DNA (such as STSs)hat are connected by any length of physical overlap.or the model data sets shown in the previous sections,map will assemble a supercontig and generate a basicntegrated map as shown in Fig. 4.

4. cDNA, genetic marker, and other supplementalata sets. These data sets can be treated in a wayimilar to YACs and have a format similar to the YAC-o-cosmid inter-Alu data. In Fig. 5, the cDNA andarker data for a model are shown in this format.5. Annotated data sets and comprehensive integratedaps. The following mapping data sets have beenerged into our integrated map as annotations: preor-

ered and floating STS data, genetic mapping data,

FIGURE 3

FIGURE 4

adiation hybrid data, and EST data. The STS dataave the following format:

input_file:5[input_sentence. . .] ENDinput_sentence:5 STS [YAC. . .]; [YAC. . .] . [order]

nd where STS, as a cosmid or pseudo-cosmid, is rep-esented by an alphanumeric string. The YACs in therst sequence before the semicolon are PCR positive.he YACs in the second sequence after the semicolonre PCR negative. The “order” after the period indi-ates a relative order of STSs. “Order” should be aonzero integer. For example, we have the followingTS data file shown in Fig. 6 for the model.

The genetic mapping location data file has the fol-owing format:

input_file:5[input_sentence. . .]input_sentence:5 cosmid locationwhere location is a string with a unit of centimorgans

cM). For example, in the given model, we will have theollowing genetic mapping location file: C7 20. Thisata file has only one input_sentence. It means that C7as a genetic location of 20 cM. If there is only anpproximate location, a tilde is used. For example, C720.

From the cosmid-to-cosmid data, YAC-to-cosmidata, STS data, and genetic location data, Imap willenerate the map shown in Fig. 7. Users have theption to show or hide the STS order numbers.On the first line of the integrated map, beginningith the abbreviation cM, a number represents theenetic distance in centimorgans from the centromeref the chromosome. There is only one cosmid, C7, thatas a known genetic position, 20 cM, in this model. Theecond line, beginning with cR (centiray), gives theadiation hybrid location. The data are empty in thisample.Then in the left margin above the cosmid hybridiza-

ion matrix, this map contains a list of YAC namese.g., Y3). The bottom block of the map contains a

atrix of cosmids (e.g., C12) reading in the same orderrom left to right and from top to bottom. A positiveybridization is indicated as a dash at the intersectionetween a YAC and a cosmid or between cosmids. Aapital “S” indicates that part of the cosmid sequence is

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n STS. Testing by PCR back to the YACs is indicatedy a small “s”, “1”, “o”, “-”, “n”, or empty space.s, STS is positive, but cosmid did not hybridize to

AC inter-Alu PCR probe; n, STS is negative and cos-id did not hybridize to YAC inter-Alu PCR probe

sometimes, an empty space is used instead of “n”); 1,TS is positive and cosmid did hybridize to YAC inter-lu PCR probe; o, STS is negative although cosmid didybridize to YAC inter-Alu PCR probe; -, STS was notested; cosmid did hybridize to YAC inter-Alu PCRrobe. An empty space means that the STS was notested and the inter-Alu PCR test was negative.

Asterisks (*) refer to cosmid self-hybridization andorm a diagonal in the matrix. The cosmid hybridiza-ion matrix consists of five cosmid contigs in thisodel.

lgorithms Underlying the IMP

1. Double search contig assembly. This algorithmZhang et al., 1994), the foundation of this package, isutlined for the reader’s convenience. It is based on aouble breadth-first search. Given a set of matrixross-hybridization data, starting at any probe as aoot to build a search tree, any clone (probe or non-robe) that overlaps the root is put in the first layerf the tree, and this clone and the root are connectedy adding an edge between them. Then a search isone for every clone in the first layer, any clone (not

FIGURE 7

et on the tree) that overlaps a clone of the first layereing put in the second layer, and it and its parent inhe first layer are connected by adding an edge be-ween them. This process is repeated for the clonesn the second layer, the third layer, and so on untilll clones have been searched. All the clones on theree will form a contig. The boundary clones of theontig will be put on the tree last. By initiating aecond breadth-first search from the boundarylones, the boundary clones on the other side will bebtained. Then alternative spanning paths will beenerated, which will become the backbone for therdering of all the clones in the contig. This algo-ithm is very fast and effective. A matrix has beenhosen to represent the ordered contig, but the formf the output can be customized. For example, theolumn coordinate can represent the probes and theow coordinate the nonprobes. As an alternative, aquare matrix with identical coordinates for alllones can be used without distinguishing betweenrobe and nonprobe. The ordered contig matrix rep-esentation allows one to: (1) visualize the location ofll the clones in the contig, (2) identify “weak points”n the contig where there are a relatively small num-er of connections, (3) identify outliers, which mayndicate erroneously assigned hybridizing pairs, and4) visualize the false positives and/or repeat units. Aouple of techniques have been used to reduce theoise level (Zhang et al., 1994). All the cosmid con-igs in the human chromosome 13 mapping projectave been generated by this algorithm. A mathemat-

cal graph, which can be abstracted from the hybrid-zation data (Zhang et al., 1994), has been studiedMcMorris et al., 1999).

2. Contig assembly with cDNAs, genetic markers, andACs. To incorporate cDNA, genetic marker, andAC data into cosmid contigs, the primary contigssembly algorithm has been enhanced in the follow-ng two aspects. First, cDNAs and genetic markersre allowed to participate in the process of cosmidontig assembly. In the double search cosmid contigssembly, cDNAs and genetic markers are consid-red the same as cosmids except in the output for-at. For example, given a cosmid and geneticarker layout as shown in Fig. 8, the marker m1 is

quivalent to a cosmid that overlaps with cosmids a2nd a4. The data of cosmid hybridization, geneticarker, and cosmid contig were obtained as shown

n Fig. 9.

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From the double breadth-first searches, several pos-ible spanning paths will be generated. According tohe order of the cosmids on the spanning path, therder of all the cosmids in the contig can be decidedZhang et al., 1994). In general, there are several pos-ible orders. From those orders, one is chosen that bestts the data using a minimum target function that wille a weighted sum of the number of “holes” in the mapf cosmids, YACs, cDNAs, and genetic markers. Weall a discontinuous point between consecutive overlapymbols a hole. In the sample map, cosmid 133C1 has

hole at the intersection with cosmid 38A12, andDNA125 has three holes at the intersections withosmids 61F10, 118D2, and 106F7. The algorithm al-ows flexibility in assigning weights to different cloneypes. In our human chromosome 13 maps, the weightssigned to cDNAs and genetic markers is 10 timestronger than the weight assigned to YACs.3. Contig assembly with ordered STSs. To incorpo-

ate ordered STS data into contigs, an alternative con-ig assembly algorithm has been developed. At Colum-ia, some cosmids have been used to generate STSs.he ends of the cosmid inserts are sequenced to designCR primers. PCR assays are then performed onACs, to validate the cosmid/YAC correspondence andlso to generate the relative order of the STSs (cos-ids). In addition, we have incorporated ordered STSs

n chromosome 13 from outside databases (e.g., White-ead Institute STS content map) into our integratedhromosome 13 maps. PCR assays are performed onhe cosmid library to determine the overlaps among theTSs and cosmids. Thus, we deal with the STSs asseudo-cosmids. We use the ordered STSs as a scaffoldn which to order other cosmids in a manner similar tordering cosmids by using the spanning path as aramework. An equally weighted order scheme haseen adopted, as follows.

FIG. 9. (a) Cosmid hybridization data. (b) Genetic marker data.c) Cosmid contig assembled without the participation of genetic

arker m1. (d) Cosmid contig assembled with the participation ofenetic marker.

3.1. Every ordered STS (cosmid and pseudo-cosmid)s given a weight corresponding to its order. The firstas weight 1, the second has weight 2, and so on.3.2. Each remaining cosmid is inspected separately.

f it overlaps with one or more cosmids and pseudo-osmids that have been weighted, we count its weights the average of the weights of those cosmids andseudo-cosmids that it overlaps. If a cosmid does notverlap any weighted cosmids or pseudo-cosmids, as-ignment of a weight will be delayed until some of theosmids and pseudo-cosmids that it does overlap haveeen assigned weights.3.3. According to the weights, the cosmids and pseu-

o-cosmids are reordered.3.4. To refine the order, the weights for each cosmid

nd pseudo-cosmid are assigned based on their overallrder. A new order is obtained from the weights. If theew order is the same as the former one, a stable orderas been attained. Otherwise the weights are re-ounted and reordered until a stable order has beenbtained.

4. Statistical distance supercontig assembly. Thislgorithm can order and orient multiple adjacent cos-id contigs into supercontigs on multiple YACs. From

he maximum-likelihood statistical distances (identicalo those that Mott et al. (1993) have used for physicalapping in a different context), the average maximum-

ikelihood statistical distances (according to the YAC-o-cosmid inter-Alu-PCR hybridization data) betweenhe contig halves (each contig is divided equally intoeft and right halves for the purpose of calculation ofverage maximum-likelihood distances) have been in-roduced to form the base for supercontig assembly.he algorithm consists of the following steps.

4.1. One randomly chosen contig is placed into annitial supercontig.

4.2. The average maximum-likelihood statistic dis-ances (Ads) between the boundary halves of the su-ercontig and all the halves of the cosmid contigs thatre not yet put in the supercontig are calculated (theormulae for calculations are defined below).

4.3. The contig with the shortest distance is placednto the supercontig, the order (left side or right side)nd the orientation of the contig depend on the combi-ation of the halves. (If the shortest distance is fromhe pair at the left boundary half of the supercontignd the left half of some tested cosmid contig, the latterontig is put on the left side of the supercontig witheverse orientation.)4.4. Steps 4.2 and 4.3 are repeated until no further

ontigs can be placed within the first supercontig or thehortest distance becomes greater than some thresholdalue.4.5. For the remainder, another supercontig is initi-

ted.4.6. The process is repeated until no contigs remain.

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Definition 1, SD (the statistical distance between twoingle cosmids): Given cosmids a and b,

SDab 5 1 2No. YACs hybridizing both a and bNo. YACs hybridizing either a or b.

Definition 2, AD (average statistic distance betweenwo cosmid groups): Given cosmid groups A and B, Aonsists of cosmids a1, a2, a3,. . ., an and B consists of b1,2, b3,. . ., bm, such that

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In our IMP, there are two user-defined thresholdalues for supercontig assembly. One is for a singleton,he single cosmid contig; the other is for nonsingletonontigs. To put a contig into a supercontig, the requiredistance (threshold value) is shorter for singletonshan for nonsingletons.

5. Supercontig assembly with ordered STSs as scaf-old. If there are at least two ordered STSs in a cos-id contig (including pseudo-cosmids), the orientation

f this contig has been fixed by the order of the STSs. Ifhere are at least two contigs with fixed order andrientation in a supercontig, they are used as a scaffoldo generate an ordered supercontig. An alternative “su-ercontig assembly with scaffold” algorithm has beeneveloped. This algorithm consists of two iterativeteps.

5.1. Suppose there are n ordered and oriented con-igs as scaffold. In placing one extra contig in thecaffold, there will be n 1 1 possible locations: beforehe first, between the first and the second, betweenhe second and the third, and so on until after theast. In addition, the orientation should be deter-

ined. There are therefore a total of 2(n 1 1) con-gurations. The local AD is calculated to find outhich one is the most favorable. For example, for the

onfiguration with location between the first and theecond on a reverse orientation, the local AD is halff the sum of the AD between the right half of therst and the right half of the extra and the ADetween the left half of the second and the left half ofhe extra. For each boundary position, only one ADetween the halves needs to be calculated. For ex-mple, for the left boundary with normal orientation,he local AD is the AD between the right half of thextra and the left half of the first. Given each extraontig, the most favorable configuration can be cho-en by calculating all local ADs. From all extra con-igs, one can be chosen with the closest, most favor-ble configuration. This is referred to as the “mostavorable contig.”

5.2. The most favorable contig is put into its mostavorable configuration to form a new scaffold. Then

tep 5.1 is repeated until all contigs have been lo-ated. In the iteration of step 5.1, some previouslyalculated local ADs can be saved. The penalty foreversal of ordered STSs can be incorporated into theocal ADs.

6. Algorithm used in Ymap. Given a set of YACnter-Alu cosmid hybridization data, an algorithm thatill generate the orders of all YACs and cosmids is

mplemented as follows. The user defines the thresholdalue that will determine if the two YACs overlap. Ifhe data are relatively pure, two YACs that share oneosmid can be considered overlapping. For noisy data,he user can set the threshold value greater than 1, forxample, 2. Thus the overlaps among the YACs will bebtained. By a double search algorithm, spanningaths are obtained. From these spanning paths, therder of the YACs can be determined by using weight-rder iteration as presented in section 3. To obtain therder of the cosmids and to refine the order of theACs, an alternative iteration scheme is performed as

ollows.

6.1. Each cosmid is weighted according to the aver-ge orders of YACs that it overlaps. All cosmids arerdered from their weights.6.2. Each YAC is weighted according to the average

rders of cosmids that it overlaps. All YACs are orderedrom their weights.

6.3. Steps 6.1 and 6.2 are repeated until a stablerder has been achieved. In our definition, if a newrder is identical to the previous one, a stable order haseen reached.

This algorithm can be used to determine the ordersf STSs from a set of STS content mapping data. As inhe previous case, the user should define the thresholdalue that will determine if the two clones overlap. Ifhe data are relatively pure, two clones that share oneTS can be considered overlapping. For noisy data, theser can set the threshold value greater than 1. Thushe overlaps among the clones will be obtained. Withhe above algorithm, the STS set can be treated as aosmid set and the clone set as a YAC set. The STSrder and the clone order can be determined.

RESULTS

IMP has been used to generate integrated YAC mapsor human chromosome 13 (Fischer et al., 1994; Caya-is et al., 1998), a high-resolution comprehensive phys-

cal map of the human chromosome 13q12–q13 regionontaining the breast cancer susceptibility locus BRCA2Fischer et al., 1996), and an integrated physical mapf the human chromosome 13q14 region containing theeletion in chronic lymphocytic leukemia (Kalachikovt al., 1997). These maps can be found on our WWWage (http://genome1.ccc.columbia.edu/;genome/).

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AC Maps for Human Chromosome 13

Since the supercontigs are often too extensive toisualize, YAC maps have been adopted as the basicnit for presentation of data. These YAC maps are

ntegrated components of the Columbia human chro-osome 13 database. With a Web browser, certain

eatures of the map may be selected to allow for infor-ation searches extending from the original supercon-

ig beyond its boundaries to overlapping YAC maps.here a related YAC seems to run off the map, clicking

n the “?” following that YAC’s name calls its own mapnd often allows visualization of its extension; clickingn the YAC’s name yields all the YAC inter-Alu PCRrobe hybridization data, STS-to-YAC hybridizationata, and marker (including cDNA)-to-YAC hybridiza-ion data available in the human chromosome 13 da-abase. With this approach, one can walk along thehromosome until the boundary of the supercontig—aap between YACs not spanned by any other clone—iseached. Clicking on the cosmid’s name will lead tohree sets of data, cosmid-to-cosmid, cosmid-to-YAC,nd cosmid-to-marker (including cDNA), all from theame chromosome 13 database.Integrated YAC maps, of a suitable size for view-

ng, have been chosen as windows to see the wholeap of chromosome 13. Although the integrated YACaps are not real geometric maps due to the lack of

ength information, nor real topological maps givenhe lack of absolute continuity for every DNA frag-ent, the integrated organization of different map-

ing data into one map provides a very useful pictureo visualize the complicated relationships among theifferent mapping data sets. The maps can thus beiewed as quasi-topological maps. According to theature of the data, the orders of clones and markershown in those maps, although still subject to beingodified by later experimental data, can serve as a

ood starting point for further analysis at the gene orequence level.At the time of writing, we have generated more than

00 integrated YAC maps and an equal number ofAC-to-cosmid maps. We present here one integratedAC map, that of Y841a5 (Fig. 10).The remaining YACaps can be found through our Web page. Fifty-five

osmids are hit by YAC 841a5, there are nine otherACs hitting those 55 cosmids, and 27 cDNAs andarkers are related to those cosmids. Among the 55

osmids, 13 have been used to generate STSs. On therst line of this YAC map, labeled cM, a number rep-esents the genetic distance from the centromere. InAC map Y841a5, cosmid 106F7 has a genetic distancef 23 cM relative to the centromere on the q arm ofhromosome 13. Similarly, on the second line labeledR, a number represents the radiation hybrid distancerom the centromere. In map Y841a5, cosmid 15F8 has

radiation hybrid distance of 70 cR and 106F7 has aadiation hybrid distance of 74 cR. On the third line, antag indicates that the cosmid in the same column has

een used to generate an STS. On the Sequence line, a-” indicates that an end, usually T7, of the cosmid inhe same column has been sequenced. On the lineeginning with the tag q12.2, a “-” indicates that theosmid has been mapped in situ to 13q12.2. On thepper half of the upper block of the map, in lineseginning with marker or cDNA names, a “-” is used toenote hybridization between the marker or cDNA andhe corresponding cosmid. In map Y841a5, marker13S120 hybridizes to cosmids 10E12 and 69G12,hile cDNA125 hybridizes to cosmids 61F10 and06F7. On the lower half of the upper block of the map,n lines beginning with YAC names, a “-” signifiesybridization between the YAC and the correspondingosmid. Since it is a map of YAC 841a5, this YACverlaps all 55 cosmids. YAC 930e2 overlaps 28 cos-ids. Nine of them have been used to generate STSs.our of those 9 cosmids, 110A10, 104G4, 116F7, and24A7, were not hit by YAC 930e2 Alu-PCR hybridiza-ion, although their STS assays were positive. Such aiscrepancy could be due to insufficient representationf inter-Alu PCR products in this portion of the YAC,alse-positive PCR results, or deletions within theAC. This YAC map consists of 6 cosmid contigs, theirrder generated by IMP.

Physical Map of the Human Chromosome 13q12–q13 Region

By assembling various types of physical mapping data,high-resolution annotated physical map of a 4-Mb re-

ion of human chromosome 13 that includes the BRCA2ocus has been generated. This map consists of a YAContig with 42 members spanning the 13q12–q13 re-ion and aligned contigs of 399 cosmids established byross-hybridization between the cosmids, which wereelected from a chromosome 13-specific cosmid librarysing inter-Alu PCR probes from the YACs. The endequences of 60 cosmids spaced nearly evenly acrosshe map were used to generate STSs, which wereapped to the YACs by PCR. A contig framework was

enerated by STS content mapping, and the map wasssembled on this scaffold. Additional annotation wasrovided by 72 expressed sequences and 10 geneticarkers that were positioned on the map by hybrid-

zation to cosmids. For additional details, see the BRCA2ap (December 1995) at our Human Chromosome 13eb page (http://genome1.ccc.columbia.edu/;genome/)

Fischer et al., 1996).

Physical Map of the Human Chromosome 13q14Region

An integrated high-resolution annotated map ofAC, PAC, and cosmid contigs has been constructed.his map covers 600 kb of the 13q14 genomic regionhere an unknown tumor suppressor gene for B-cell

hronic lymphocytic leukemia (CLL) is expected. Inddition to densely positioned genetic markers andTSs, this map was further annotated by localization

otss1ga

ilGssA

eohptamcacvwctafil

85PHYSICAL MAPPING SOFTWARE PACKAGE

f 32 transcribed sequences (ESTs) using a combina-ion of exon trapping, direct cDNA selection, sampleequencing of cosmids and PACs, and homologyearches. For further details, see the B-CLL map (July997) at our Human Chromosome 13 Web page (http://enome1.ccc.columbia.edu/;genome/) (Kalachikov etl., 1997).

DISCUSSION

The suite of programs in IMP has been developed toncorporate additional types of experimental data col-ected from external databases and from the Columbiaenome Center. The new features of IMP include: (1)

upercontig assembly, (2) gap minimization, (3) STScaffolding, (4) cDNA localization, and (5) optimization.

statistical distance approach has been used to gen-

FIGU

rate supercontigs from adjacent cosmid contigs basedn the YAC and cosmid inter-Alu PCR data. To en-ance the contig assembly program, an optimizationrocedure was adopted by assigning weighted penal-ies for the number of holes in the upper panel. Wessign strong weights to ungapped cDNAs and geneticarkers and weak weights to ungapped YACs. To in-

orporate the ordered STS data in the maps, threedditional algorithms have been used. Inside a cosmidontig, an iterative optimization protocol has been de-eloped, using the ordered STSs as an initial frame-ork, to generate the best cosmid order. Among the

osmid contigs, a penalty function has been added tohe statistical distance. If enough ordered STS data arevailable to form a scaffold, a best statistic distancetting will be performed. To eliminate false positive

inks caused by repeat units or chimerism, a mecha-

10

RE

nctnwttdstt

cD

ncspnarm

86 ZHANG ET AL.

ism is provided to indicate “bad” YACs. There are twoustomizable factors. One is the minimum length andhe other is the maximum number of holes (or relativeumber of holes). For example, if a YAC hybridizesith only one or two cosmids, it is very possible that

he YAC does not belong to the region. Users can adjusthe length and holes to fit their needs. IMP has beenesigned to generate reasonable maps from noisy dataets. The algorithms used by IMP have polynomial-ime complexity. Most are linear or quadratic. An in-egrated map of about 500 cosmids, 100 YACs, and 50

FIGURE 1

DNAs will be generated in a couple of minutes on aEC Alpha workstation.IMP also provides a few plain file database mainte-

ance functions: merger, with which new sets of dataan be merged into the database; filter, with which apecific data set can be selected from the database; andurifier, with which a subset of the data can be elimi-ated from the database. Furthermore, an option isvailable for copying this plain file database into aelational database management system, to make theanipulation of the data more flexible and more effec-

Continued

0—

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cpstImpadsrcuttshdwdutt

g

A

B

C

C

C

C

C

F

F

F

G

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K

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M

O

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P

S

S

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Z

87PHYSICAL MAPPING SOFTWARE PACKAGE

ive. We are using IMP database maintenance func-ions and SYBASE, a relational database managementystem, to maintain chromosome 13 mapping data.eb browsers have been used as user interfaces for the

uman chromosome 13 database and the databases forene identification projects.We have developed an integrated physical mapping

omputer software package (IMP) designed to supporthysical mapping of human chromosome 13 as well aseveral gene identification projects based on the posi-ional candidate approach. In the previous sections,MP was presented and details concerning data for-ats, maps, and the underlying algorithms were ex-

ounded. We have described two applications in highlynnotated maps which were constructed by IMP inisease gene loci. Our methodology is a natural expan-ion of developments in physical mapping (see Prim-ose, 1995). The ability to handle noisy data is a keyriterion for physical mapping software. The algorithmsed to construct the cosmid hybridization matrix hashe ability to deal with false negatives and false posi-ives (Zhang et al., 1994). Since the algorithm for theupercontig assembly is based on the maximum likeli-ood statistical distance, it is a tool for managing noisyata. In our experience, for a low-noise data set, IMPill generate a high-quality map; for a modestly noisyata set, a reasonable map can be expected. We havesed IMP to generate local maps for several gene iden-ification projects; in fact, IMP has become a routineool in our research.

ACKNOWLEDGMENTS

The authors are grateful to referees for their comments and sug-estions.

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