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GENERATING GENOMICS DATA 11 called shotgun sequencing. The redundancy of 6–10-fold is required both to ensure the correct sequence is determined free of errors (by verifying the nature of any particular base using independent sequence reads) and to attempt to cover the whole fragment. If the whole of the fragment were equally clonable, then 6-fold coverage would be required to ensure that all regions of the fragment are sampled. In practice, not all regions are equally clonable, and assembly of shotgun reads is usually followed by a ‘finishing’ phase where missing regions are sequenced by more directed methods (such as primer walking) and ambiguities in the sequence are clarified. Sequencing a genome using a physical map thus involves a large number of small shotgunassembly-finishing projects based on a set of clones that overlap minimally and cover the whole (or most of) the genome. The regions of overlap between the clones serve to add confirmation to the determined sequence and to the mapping process. Rather than build a map first, a map-asyou-go strategy has been proposed, utilizing extensive GSS data from large insert clones. The BAC or other library is first completely end sequenced to yield one GSS every 5–10 kb of the genome on average. A BAC clone is selected (at random) and shotgun sequenced. Using the finished sequence, a new clone that minimally overlaps is selected on the basis of end sequence comparison. In this manner, the project can ‘walk’ from each seed clone into the flanking genome. Shotgun sequencing of whole chromosomes and whole genomes Initially, the complexity of genomic DNA, in particular the presence of local and disseminated repeats, suggested that it might not be possible to assemble and finish fragments of DNA larger than 100 kb. The limits were set by the efficiency of the computer algorithms used for assembly, and the overall sequencing strategy. However, with advances in sequencing, algorithms and computing power, it is now possible to assemble even the largest genomes in one go, from tens of millions of individual reads (Figure 1.1). The success of the clone-by-clone strategy is based in part on its breaking down of the assembly problem in to a set of smaller, manageable ones. For many protozoan parasites, chromosomes can be separated by PFGE. These chromosomes, ranging from 100 kb to over 7 Mb, can also be shotgun sequenced and thus the whole-genome assembly is reduced to a set of smaller projects. Chromosome shotgun sequencing is now a mainstay of many protozoan genome sequencing projects. For organisms with larger genomes, where chromosomal separations are not possible, the success of the human (3000 Mb) and Drosophila melanogaster (160 Mb) whole genome shotgun assemblies suggests that this method should also be applicable to metazoan parasites. For whole genome and whole chromosome shotgun projects, the large numbers of reads from small insert (2 kb) clones are usually supplemented with reads from shotgun libraries with larger mean insert size (10 kb and above; BAC end GSS are also used). These longer clones serve as a scaffold that is used to orientate and affirm the assembly made with the 500 bp reads from the 2 kb clones. If a sequence contig suggests that the two ends of a large insert clone are too close to each other, it is likely to be in error. Similarly, large-insert sequences can serve to link contigs derived from shotgun sequencing. The whole genome shotgun method requires a large amount of data to be effective. For a 100 Mb genome (such as is found in many nematode parasites) a one-fold shotgun is MOLECULAR BIOLOGY
Chromosomal/genomic DNA segment RANDOM SHEARING Fragmented DNA of a particular size class Insert DNA CLONING Vector DNA Shotgun library ‘right end’ sequencing primer ‘left end’ sequencing primer Shotgun sequencing SEQUENCE ASSEMBLY Sequence contigs SEQUENCE FINISHING GAP SINGLE STRANDED GAP Clone abc left Clone abc right Shotgun clone abc Clone xyz left Clone xyz right Large-insert clone xyz
Page 2 and 3: Molecular Medical Parasitology
Page 4 and 5: Molecular Medical Parasitology Edit
Page 6 and 7: Contents List of contributors Prefa
Page 8 and 9: List of contributors Mark Blaxter,
Page 10 and 11: LIST OF CONTRIBUTORS ix Richard. J.
Page 12 and 13: Preface Parasitology was born as th
Page 14 and 15: S E C T I O N I MOLECULAR BIOLOGY
Page 16 and 17: C H A P T E R 1 Parasite genomics M
Page 18 and 19: TABLE 1.1 Parasite genomes: genome
Page 20 and 21: GENERATING GENOMICS DATA 7 differen
Page 22 and 23: GENERATING GENOMICS DATA 9 TABLE 1.
Page 26 and 27: GENERATING GENOMICS DATA 13 200 000
Page 28 and 29: BIOINFORMATICS AND THE ANALYSIS OF
Page 30 and 31: THE POST-GENOMICS ERA, AND THE OTHE
Page 32 and 33: THE PARASITES AND THEIR GENOMES 19
Page 40 and 41: FURTHER READING 27 treated with a d
Page 42 and 43: C H A P T E R 2 RNA processing in p
Page 44 and 45: TRANS-SPLICING 31 cis trans Exon 1
Page 46 and 47: TRANS-SPLICING 33 snRNPs (small nuc
Page 48 and 49: TRANS-SPLICING 35 trans cis U2AF35
Page 50 and 51: RNA EDITING 37 P AAAA... AAAA... AA
Page 52 and 53: RNA EDITING 39 entire transcript re
Page 54 and 55: RNA EDITING 41 5 Editing block C Ed
Page 56 and 57: RNA EDITING 43 microscopy) approach
Page 58 and 59: FURTHER READING 45 Nilsen, T.W. (19
Page 60 and 61: C H A P T E R 3 Transcription Arthu
Page 62 and 63: UNUSUAL MODES OF TRANSCRIPTION IN T
Page 64 and 65: CLASS I TRANSCRIPTION IN TRYPANOSOM
Page 70 and 71: CLASS II TRANSCRIPTION OF PROTEIN C
Page 72 and 73: SL RNA AND U snRNA GENE TRANSCRIPTI
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SL RNA AND U snRNA GENE TRANSCRIPTI
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SL RNA AND U snRNA GENE TRANSCRIPTI
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FURTHER READING 65 and switching in
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C H A P T E R 4 Post-transcriptiona
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POST-TRANSCRIPTIONAL REGULATION IN
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FURTHER READING 87 inhibition, it m
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C H A P T E R 5 Antigenic variation
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ANTIGENIC VARIATION AT THE STRUCTUR
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ANTIGENIC VARIATION AT THE STRUCTUR
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VSG Signal sequence Amino-terminal
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GENETICS OF ANTIGENIC VARIATION 97
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IMMUNE RESPONSES TO TRYPANOSOME INF
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INNATE RESISTANCE, HUMAN SUSCEPTIBI
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ANTIGENIC VARIATION IN MALARIA 107
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FURTHER READING 109 ACKNOWLEDGEMENT
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C H A P T E R 6 Genetic and genomic
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‘FORWARD’ VS. ‘REVERSE’ GEN
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EXAMPLES OF ‘FORWARD’ GENETIC A
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EXAMPLES OF ‘FORWARD’ GENETIC A
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REVERSE GENETICS AND LEISHMANIA VIR
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VALIDATION OF CANDIDATE VIRULENCE G
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S E C T I O N II BIOCHEMISTRY AND C
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C H A P T E R 7 Energy metabolism P
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SUBCELLULAR ORGANIZATION OF AMITOCH
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CELL MEMBRANE Fructose [b] Glucose
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STEPS OF AMITOCHONDRIATE CORE METAB
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ENZYMATIC DIFFERENCES BETWEEN AMITO
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ACTION OF NITROIMIDAZOLE DRUGS 135
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ENVOI 137 unambiguously reflect the
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FURTHER READING 139 Saavedra-Lira,
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THE EMBDEN-MEYERHOF-PARNAS (EMP) PA
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WHY DO TRYPANOSOMATIDAE HAVE GLYCOS
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FURTHER READING 153 for use in agri
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CARBOHYDRATE METABOLISM 155 as a hu
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158 ENERGY METABOLISM - APICOMPLEXA
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172 PROTEIN METABOLISM PROTEINS (1)
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174 PROTEIN METABOLISM the C-termin
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176 PROTEIN METABOLISM and also, to
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178 PROTEIN METABOLISM values rangi
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180 PROTEIN METABOLISM of the plasm
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182 PROTEIN METABOLISM in vivo, in
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184 PROTEIN METABOLISM PROLINE Poly
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186 PROTEIN METABOLISM CO 2 Dec-SAM
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188 PROTEIN METABOLISM a cMDH has b
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190 PROTEIN METABOLISM Urea ARGININ
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192 PROTEIN METABOLISM been describ
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194 PROTEIN METABOLISM absence of g
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198 PURINES AND PYRIMIDINES enable
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200 PURINES AND PYRIMIDINES provide
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202 PURINES AND PYRIMIDINES activit
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204 PURINES AND PYRIMIDINES physiol
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206 PURINES AND PYRIMIDINES Amitoch
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208 PURINES AND PYRIMIDINES their a
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210 PURINES AND PYRIMIDINES FIGURE
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214 PURINES AND PYRIMIDINES protein
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220 PURINES AND PYRIMIDINES in Plas
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222 PURINES AND PYRIMIDINES whereas
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226 TRYPANOSOMATID CARBOHYDRATES AF
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228 TRYPANOSOMATID CARBOHYDRATES In
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230 TRYPANOSOMATID CARBOHYDRATES po
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232 TRYPANOSOMATID CARBOHYDRATES LP
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234 TRYPANOSOMATID CARBOHYDRATES re
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236 TRYPANOSOMATID CARBOHYDRATES sc
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A. sAP COOH [T][T](S/T)(S/T)(S/T)SS
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240 TRYPANOSOMATID CARBOHYDRATES Cr
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242 INTRACELLULAR SIGNALING protein
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244 INTRACELLULAR SIGNALING FIGURE
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246 INTRACELLULAR SIGNALING 212.5 2
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248 INTRACELLULAR SIGNALING cycle.
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250 INTRACELLULAR SIGNALING V-H -A
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252 INTRACELLULAR SIGNALING domain)
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254 INTRACELLULAR SIGNALING the kno
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256 INTRACELLULAR SIGNALING from in
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258 INTRACELLULAR SIGNALING FIGURE
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260 INTRACELLULAR SIGNALING ESAG4 (
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262 INTRACELLULAR SIGNALING and ind
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264 INTRACELLULAR SIGNALING ATPase
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266 INTRACELLULAR SIGNALING G11XG
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268 INTRACELLULAR SIGNALING a diffe
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270 INTRACELLULAR SIGNALING rapid l
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272 INTRACELLULAR SIGNALING cells w
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274 INTRACELLULAR SIGNALING PfPPJ i
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276 INTRACELLULAR SIGNALING is cont
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278 PLASTIDS, MITOCHONDRIA, AND HYD
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298 HELMINTH SURFACES Important exc
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300 HELMINTH SURFACES protease inhi
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302 HELMINTH SURFACES volume, there
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304 HELMINTH SURFACES projecting si
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306 HELMINTH SURFACES schistosome t
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308 HELMINTH SURFACES re-establish
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310 HELMINTH SURFACES hepatic cells
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312 HELMINTH SURFACES lungs. Larvae
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314 HELMINTH SURFACES cuticle, whic
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316 HELMINTH SURFACES cuticle in th
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318 HELMINTH SURFACES mec-8 or sym-
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320 HELMINTH SURFACES current, when
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322 HELMINTH SURFACES The cuticle-h
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324 HELMINTH SURFACES are typically
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326 HELMINTH SURFACES or carrier pr
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328 HELMINTH SURFACES of tyrosine-b
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330 HELMINTH SURFACES blood. The ge
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332 HELMINTH SURFACES Mutations in
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334 HELMINTH SURFACES in the hypode
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336 HELMINTH SURFACES (including H-
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338 HELMINTH SURFACES Blaxter, M.L.
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340 ENERGY METABOLISM IN HELMINTHS
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360 NEUROTRANSMITTERS CO 2 H NH 2 G
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362 NEUROTRANSMITTERS TABLE 15.1 An
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364 NEUROTRANSMITTERS more arms tha
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366 NEUROTRANSMITTERS the surroundi
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368 NEUROTRANSMITTERS proteins requ
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370 NEUROTRANSMITTERS FIGURE 15.11
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372 NEUROTRANSMITTERS FIGURE 15.13
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374 NEUROTRANSMITTERS sensitivity t
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376 NEUROTRANSMITTERS TABLE 15.3 Cl
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378 NEUROTRANSMITTERS crossed the c
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380 NEUROTRANSMITTERS have been tes
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382 NEUROTRANSMITTERS C-terminals a
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384 NEUROTRANSMITTERS Davis and Str
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386 NEUROTRANSMITTERS Cephalic gang
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388 NEUROTRANSMITTERS myoexcitation
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390 NEUROTRANSMITTERS is phosphoryl
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392 NEUROTRANSMITTERS many other an
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398 DRUG RESISTANCE of some of the
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400 DRUG RESISTANCE It is now estim
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402 DRUG RESISTANCE is again assume
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404 DRUG RESISTANCE An alternative
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406 DRUG RESISTANCE clinical eviden
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408 DRUG RESISTANCE FIGURE 16.4 Fol
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410 DRUG RESISTANCE Using similar s
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412 DRUG RESISTANCE whether these r
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414 DRUG RESISTANCE FIGURE 16.5 Str
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416 DRUG RESISTANCE FIGURE 16.6 Try
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418 DRUG RESISTANCE has permitted e
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420 DRUG RESISTANCE FIGURE 16.7 Dru
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422 DRUG RESISTANCE near future. Th
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424 DRUG RESISTANCE million new inf
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426 DRUG RESISTANCE some 50 million
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428 DRUG RESISTANCE pharynx, the bo
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430 DRUG RESISTANCE efforts for glo
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432 DRUG RESISTANCE and resistance
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434 MEDICAL IMPLICATIONS TABLE 17.1
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436 MEDICAL IMPLICATIONS TABLE 17.1
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438 MEDICAL IMPLICATIONS transmissi
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440 MEDICAL IMPLICATIONS H 2 C H H
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442 MEDICAL IMPLICATIONS parasites
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444 MEDICAL IMPLICATIONS neuropsych
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446 MEDICAL IMPLICATIONS of toxic f
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448 MEDICAL IMPLICATIONS Future con
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450 MEDICAL IMPLICATIONS Melarsopro
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452 MEDICAL IMPLICATIONS prevention
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454 MEDICAL IMPLICATIONS resulting
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456 MEDICAL IMPLICATIONS for S. ste
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458 MEDICAL IMPLICATIONS are though
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460 MEDICAL IMPLICATIONS FIGURE 17.
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462 MEDICAL IMPLICATIONS Marr, J.J.
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464 INDEX Aldolase, 143 Plasmodium
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466 INDEX Ascofuranone, 143 ASCUT-1
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468 INDEX Cnidarians, RNA trans-spl
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470 INDEX Entamoeba, 125 Ca 2 metab
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472 INDEX Glucose-6-phosphate dehyd
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474 INDEX Ivermectin, 398, 427, 455
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476 INDEX Maduramicin, 412 Major va
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478 INDEX Nitric oxide: neurotransm
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480 INDEX calcium-binding proteins,
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482 INDEX Pyrimidine transport, 200
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484 INDEX Succinate:rhodoquinone ox
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486 INDEX genome, 21 survey sequenc
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488 INDEX U small nuclear RNA (snRN
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