Running head: Computer-driven RNA Fingerprinting

Keywords: Genetics, Computer Simulation, Sequence Analysis, Differential Display, RNA Fingerprinting
 
 

    Computer simulations of RNA fingerprinting PCR experiments were run on custom made human and murine nonredundant (nr) nucleotide databases generated as described in the Systems and Methods section.

Simulations in a human database - In a first series of simulations (MAN12.8) 10,000 12-character strings were generated at random, to represent dodecanucleotide primers, with the initial requirement that they contain 8 C's or G's and 4 A's or T's, reflecting higher C and G contents in coding regions than in 3' untranslated regions. Primers containing either stop codons (TAA, TAG, TGA) in the sense strand or ³ 4 homonucleotide stretches (AAAA, CCCC etc.) were discarded (criteria a and b, respectively). Also discarded were primers with palindromic 5' and 3' ends, ³ 4 successive complementary bases long (criterion c). The above criteria were aimed at biasing the primers towards the coding sequences (CDS, a), and at enhancing the efficiency of PCR experiments (b and c).
    The first 1,000 primers considered acceptable according to criteria a, b, and c, were challenged against the human nr database (2,085-sequences, 4.4 Mb total DNA, 72.8% CDS, see Materials and Methods), to simulate the PCR. Primers yielding >100 but <250 simulated PCR products were considered adequate (114 primers); 345 primers were labeled as inefficient and 44 as "too efficient" (primers yielding an excessive number of products might target repetitive or low complexity templates, and result in low amplification efficiencies in the experimental phase); 497 primers were excluded because they contained >5 out of 8 bases at the 3' end identical to a previously accepted primer (criterion d). This criterion was aimed at reducing the chance of repeatedly targeting the same sequences.
    Figure 1A illustrates a histogram of simulated PCR product numbers obtained with the series of 503 accepted primers based on criteria a-d. Also illustrated is the probability density function (%) computed for the same set of primers against a randomly scrambled sequence database (see the Appendix for details on the computation of this curve). The observed distribution is nonrandom, featuring markedly overcrowded shoulders. This indicates the existence of significant numbers of particularly inefficient primers, as well as of particularly efficient ones, and points to the possibility of selecting a panel of valuable PCR primers based on the present "simulated gene fishing" approach.
Simulations in a mouse database - The same panel of 1000 primers, generated for simulations in the human database, was tested on the mouse nr nucleotide database (MOUSE 12.8), containing 1041 sequences comprised of 2.95 Mb total nucleotide sequence, (72.5% CDS), in order to check whether species-specific features in sequence composition played a major role in determining the efficiency scores of different primers. In this case, 193 "good" primers and 326 inefficient or too efficient ones were obtained; 481 primers were excluded due to criterion d. The distribution of simulated PCR product numbers per primer was qualitatively similar to the one obtained in the human database. Figure 1B is a scatter plot of the number of simulated PCR products obtained in the human (x-axis) vs. the mouse (y-axis) database, with the same primers. The two sets of values correlate very well (correl. coeff. r = 0.947).

Degree of coverage of the primer panel - Next, we set out to address the issue of exhaustivity of a panel comprised of the 96 best primers. It may be argued that the primers selected because of their high efficiency in yielding simulated PCR products may be directed towards subpopulations of genetic sequences within the expressed sequence database. If this were the case, the number of transcripts not targeted by any primer (failures) should be higher than expected based on the mean number of simulated products per transcript. Figure 1C describes a simulation run on the human nr database and illustrates that this is not the case. In particular, it shows the number of primers targeting each transcript. The 96 most efficient primers yield more products in the actual transcribed sequence database than it can be calculated for a randomly scrambled transcribed sequence database. When such increase in efficiency is taken into account, theoretical distributions (dashed lines in figure 1C) fit the results of the simulations, arguing against a bias of our primer panel towards specific subpopulations of target sequences. Basically, our data speak for the existence of good and bad primers (nonrandom distribution), but fail to reveal the existence of unexpectedly good or bad templates (random distribution).

Effect of base composition of the primers - The choice of primers rich in C's and G's was driven by the goal of targeting coding regions. However, it became necessary to ascertain whether this unbalanced base composition in the primers could determine a bias in the choice of target sequences from the database. To address this issue, the same simulations described above were performed using 12-nucleotide primers composed of 6 A's or T's and 6 C's or G's (MAN12.6, MOUSE12.6). As far as coverage, the results were superimposable to those obtained with the CG-rich primer set (not shown).

Use of degenerate primers - We reasoned that the introduction of a partially degenerate position at the 3' end of each primer would lead to an increase in product numbers, and enhance the exhaustivity of our optimal primer collection. Thus, we went back and repeated computer simulations using 12-nucleotide CG-rich primers, containing a partially degenerate base (W or S) at their 3' ends (representing primer pairs, rather than single primers). As expected, the use of degenerate primers in simulations gave rise to an increased product number, with respect to nondegenerate ones (figure 2A). 

    As a result of the elaboration described above, 25 primers were synthesized and tested at the bench (12 nondegenerate, 13 degenerate), to assess the correspondence between theoretical predictions and experimental results. As templates, we used total RNA from HepG2 cells cultured in different redox conditions (Cabibbo et al., in preparation), and from embryonic and postnatal mouse brain territories (Corradi et al., in preparation; Alli et al., in progress).

Setup of optimal amplification conditions - The optimal annealing temperature for our primer panel was determined by testing different annealing temperatures in a range between 45 °C and 53 °C. The best results were obtained at 50 °C. Lower temperatures caused weaker, fuzzier banding patterns, whereas no bands were seen with 52 or 53 °C in the annealing step. The optimal number of degenerate nucleotides was also determined experimentally, by testing primers containing no degenerate position, one degenerate position at the 3' end nucleotide, or two degenerate positions at nt 11 and 12. The best and most reproducible results were repeatedly obtained with one site of partial degeneration (W or S) at nt 12, or the 3' terminal nucleotide.

Annealing of the primers to the target sequences - As a further step, we analyzed the mode of annealing in our experimental conditions with respect to the annealing constraints set in our simulations. Clones obtained by RNA fingerprinting were sequenced and aligned to nucleotide databases. Products identical to deposited cDNAs or ESTs were analyzed to determine where exactly in the transcript the primer had annealed, with how many mismatches, and where the mismatches had occurred in the primer sequence. As shown in figure 4A, the experimental annealing conditions represent an excellent approximation of the simulated ones. In only two cases out of twenty nine did a mismatch occur at any site within the last four bases at the 3' end of the oligonucleotide. As it turns out, in all other cases the last four bases of the primer and template matched perfectly, defining a 3' four-base stretch critical for annealing and elongation when using a 12mer in our PCR conditions. Throughout the remaining length of the primer, annealing could occur even in the presence of up to four mismatches.

Selectivity of primer panel for coding sequences - Thirty three "differentially displayed" bands obtained by RT-PCR with each of nine different twelvemers were cloned and sequenced, 29 of which displayed ORFs throughout their lengths. Of these, 20 represented known coding sequences or their homospecific/cross-specific homologues; 9 additional new ORFs were analyzed with the GRAIL program (Roberts, 1991), which predicted an excellent coding probability in 6 out of nine cases. The relatively low number of cloned and sequenced products makes it impossible to correlate expected and observed ratios of coding to noncoding cDNAs and, in fact, the ratio of coding to noncoding cDNAs reflects that of translated to untranslated RNA sequence in mammalians. However, it may certainly be inferred from these preliminary results that the internal primer panel described here enhances access to coding regions by two orders of magnitude when compared to the differential display approach.

Size of amplified cDNAs - Because the described amplification strategy is based on the occurrence of two copies (one sense and one antisense) of a degenerate 12-mer within a single mature transcript, we set out to determine whether our approach would lead to the preferential amplification of large size cDNAs, corresponding to large transcripts. Table 2 describes the sizes of transcripts and open reading frames from 13 published cDNAs identified through our approach. These transcripts range in size between 1.0 and 3.8 kb. The corresponding open reading frames range in size between 582 and 1914 nt. These preliminary figures suggest that small transcripts are amplified as efficiently as larger ones, likely due to (i) high levels of degeneracy in the primer annealing step, and (ii) high-frequency occurrence of specific sequence motifs in coding regions.

    The basic idea of computer assisted gene fishing, illustrated here, is to select, among random-sequence PCR primers, the most efficient and selective ones, as judged from the results of computer simulations of PCR procedures on nonredundant cDNA sequence databases. The simulation approach represents a forceful oversimplification of experimental conditions and is governed by rigid rules: in the case described here, hybridization was assumed to occur whenever a perfect 4-base match at the 3' end of the primer occurred, with no more than 3 mismatches in the remaining portion, and a relevant PCR product was assumed to occur whenever a pairing occurred on the sense strand and a second pairing occurred on the antisense strand, 100-1000 bp downstream. These assumptions overlook the occurrence of (a) highly degenerate annealing on particularly abundant mRNA species, which can yield detectable bands in PCR gels; (b) hairpins generating gaps in primer-template doublets; (c) limiting PCR reagent concentrations which may disfavor mRNA species present at low copy numbers. Still, the results reported here support the value of simulation as a rational approach to designing efficient and selective primers for RNA fingerprinting.
    The notion of employing 12-bp PCR primers selected according to criteria other than pure chance is supported by several lines of evidence. Experimentally, large differences are observed in the number of PCR products generated from the same cDNAs by "high efficiency" versus "low efficiency" primers. It is well known that the frequency of occurrence of nucleotide "words" of a given length varies widely among different types of DNA sequences (introns, coding regions, etc.) (Claverie, Sauvaget and Bougueleret, 1990). This suggests that specific 12-nt sequences, when used as PCR primers, may be endowed with widely different efficiencies and possibly selectivities for coding vs. non-coding regions. Indeed, all simulations indicate that the distributions of the number of simulated PCR products per primer markedly depart from the ones computed for a randomly scrambled database (probability density function). A large excess of particularly efficient and particularly "poor" primers was observed in all simulations, which confirms the notion that the proposed approach might help in selecting particularly favorable 12-nt primer sequences for RNA fingerprinting. When the significance of these findings is assessed by challenging the same set of primers against the human and murine sequence databases, the numbers of PCR products obtained in the two simulations correlate very well, indicating that the unexpectedly high or low efficiencies of some primers do not arise from aberrations in the composition of the databases used, but rather from intrinsic differences in efficiency among primers. In other words, this confirms that some "genetic words" have particularly high or low probabilities of occurring in coding sequences (Claverie, Sauvaget, and Bougueleret, 1990), with no major differences between phylogenetically related genomes.
    The predictivity of simulation in selecting efficient primers appears to be confirmed by the analysis of the number of PCR products experimentally obtained with a reduced panel of primers. The actual probability of hybridization under the experimental conditions described was obviously unknown, as were the exact sizes of the cDNA pools analyzed. The absolute numbers of PCR products were lower than in the simulations; however, the relative efficiencies of the tested primers were in reasonable agreement with our predictions.
    A series of criteria were adopted in selecting the random-sequence primers to be tested by PCR simulation. These criteria were aimed at excluding primers which would likely generate technical problems (e.g. those containing homo-nucleotide stretches), at biasing the primers towards coding regions (a fixed ratio of 8 C's or G's to 4 A's or T's was used, and primers containing stop codons in the sense strand were excluded) and at obtaining exhaustivity (at least four out of eight nt at the 3' end were unique for each selected primer). The last constraint turned out to be quite restrictive, in that after selecting about one hundred primers many further random sequences had to be generated in order to find a new, compatible one. Conceivably, this may lead to an enhanced coverage by our primer panel.
    As reported above, most of the PCR products cloned so far using the primers in the panel contain significant ORFs, either throughout their whole lengths, or at one end. This supports the idea that a panel of primers selected as described here should make it possible to address coding regions in mRNA in a majority of cases, permitting a prediction as to the nature of corresponding peptide sequences, and the establishment of cross-specific relationships with deposited sequences from distant phyla. Although an increasing number of transcribed sequences are deposited in nucleotide databases, catalogs are only complete for one model eukaryotic organism, and a long time may elapse before full length cDNAs become available for a significant number of organisms. Likewise, in the case of large genomic sequences deposited in the framework of organism-specific genome sequencing projects, the availability of a coding sequence tag would circumvent the need for the lengthy and error-prone process of identifying and splicing transcribed sequences at the computer. Thus, the prompt recognition of a newly discovered sequence as a member of a phylogenetically conserved gene family represents a significant advantage of our approach over previously described ones. The results described here are more relevant when one considers that, in its current version, our protocol utilizes oligo-dT-primed first strand cDNA, thus shifting the cDNA pool towards 3' ends. An experimental update in our approach will require first strand cDNA synthesis using internal primers, rather than oligo-dT primers, to further enhance targeting of coding sequences. To date, this has not been done lest a significant portion of products might contain ribosomal RNA sequences, and is the current subject of methodological work by our group.
    An additional question to address was whether the primers selected because of their high efficiency in yielding simulated PCR products were directed towards subpopulations of genetic sequences. Three criteria were considered: (i) the relation between the length of the sequences and the number of products they yielded showed no sign of clustering into subpopulations (not shown); (ii) the distributions of the number of simulated PCR products per sequence never displayed multiple modes and were generally smooth and continuous; (iii) although such distributions were shifted towards high numbers of products (especially when obtained with the sets of 96 best-performing primers) with respect to the expectations for scrambled sequence databases (Appendix), they were well fit by simply increasing the matching probability by a factor equal to the ratio [observed / expected] mean numbers of products; the latter approach also yielded adequate predictions of the percentage of non-targeted transcripts ("silent sequences"). All this argues against the existence of subpopulations of nt sequences with significantly different probabilities of being recognized by the sets of primers employed here.
    A more difficult problem to tackle is redundancy, i.e. the production of many amplification products from each transcript in the mRNA library used. A balance between redundancy and exhaustivity must be reached empirically. In our simulations, the percentage of "silent sequences" was closely approximated by the reciprocal of the mean number of products from each sequence (a measure of redundancy). Our data suggests that, as the number of products increases, the proportion of "silent sequences" decreases more slowly than expected from Poisson statistics, where the fraction of failures is predicted by exp(-mean). The departure from Poisson behavior, observed in the simulations, is predicted theoretically, based on the heterogeneous probability of being sampled for sequences with dissimilar length and composition. An experiment employing 96 primers with some 100 bands per primer would yield about 10,000 discernible PCR products; assuming that a typical cell expresses some 20,000 genes, this would correspond to an average hitting rate of 0.5 PCR products per expressed gene. According to our theoretical predictions and simulations, under this conditions coverage should approximate 13% (as opposed to the theoretical upper limit of  39% for a purely random, Poisson distribution). Thus, the method here proposed is certainly not aimed at obtaining exhaustive coverage of differential gene expression. Much higher numbers of primers (and PCR products) would be needed; for example, in order to fish out 90 % of the 20,000 expressed genes one should analyze about 200,000 bands. Classical differential display with similar numbers of PCR products should obtain similar coverage rates, since available data suggest that the probability of yielding PCR products by differential display is also markedly heterogeneous (genes expressed in high number of copies have much higher probability of yielding PCR products; Bertioli et al., 1995; our unpublished data). Thus, the main differences should not regard coverage, but bias at low coverage levels: whereas differential display tends to produce PCR products for more abundantly expressed genes, the approach here proposed appears not to be biased in this regard and to produce a high percentage of PCR products in coding regions.
    An approach similar to ours has been taken by others (Lopez-Nieto and Nigam, 1996). Those authors proposed and described a protocol employing each one of 30 computer-generated arbitrary 8-nt primers selected for their probability of occurrence in sense strands of coding sequences, to be used in combination with each of the reverse complement series, i.e. 29 primers. In the present paper, we propose the utilization of single, partially degenerate primers selected for their frequent and balanced appearance both in sense and antisense strands. The protocol proposed by Lopez-Nieto and Nigam entails the use of 30 ´ 29 = 870 PCR amplifications, as opposed to 96 in our schema. Although 870 primer combinations will provide greater coverage of the genes expressed in a given tissue sample than just 96 PCRs, from our analysis of the relationship between exhaustivity and redundancy, one would suspect that the gain may not be worth the effort (2-3 times as many expectedly targeted genes). More in general, we believe that one should probably not embark on a PCR-based differential screening project with the goal in mind of producing a complete catalog of differentially expressed genes; instead, a more sensible scope would be to generate a number of genetic tags helpful in initiating the dissection of functional pathways in development or differentiation active in one's system of choice. As a matter of fact, these pathways typically involve, in addition to differentially expressed genes, ubiquitously expressed genes as well as post-transcriptional and post-translational regulatory events. Other valuable, albeit more demanding approaches are available to those researchers who wish to generate complete catalogs, rather than obtaining a sample of differential gene expression in their biological systems (Kato, 1995). More importantly, microchip-based technology will become more generally available in the medium term for high-throughput, genome science-style studies in many organisms.
    At difference with Lopez-Nieto and Nigam’s study (1996), which described computer analysis for a large set of random primers, but focused on the experimental validation of a primer set specifically designed to target a group of G protein-coupled receptor genes, the present study has identified primers not biased towards any specific gene family, which have been utilized experimentally by a number of groups to target protein coding regions in general, and all appear to work equally efficiently in the standard experimental conditions described in the present paper (Corradi et al., 1996; Cabibbo et al., 1998; Malgaretti et al., 1997). Furthermore, the octamers need 5' adaptors to increase product numbers and high annealing temperatures (54 °C) to decrease the background of low-efficiency/truncated amplification products (Lopez-Nieto and Nigam 1996). However, this procedure might somewhat favor the recursive amplification of sequences partially or completely homologous to the adaptor. Our protocol employs longer primers, at a lower annealing temperature (50 °C), resulting in good control of the background level, and high amplification efficiencies.
    The evidence presented here demonstrates that, in our scheme, only four residues at the 3' end of our primers need to anneal with a perfect match to their templates for the polymerase chain reaction to take place, while up to four mismatches are well tolerated over the remaining eight residues. Thus, 12-mer primers such as the ones proposed in the present paper produce large numbers of products in our experimental conditions by virtue of partially degenerate annealing. Furthermore, as primer sequences used in experiments are identical to those used in simulations (no adaptors), effective lessons on how to refine the simulation strategy can be learned from the analysis of experimental results.
 In summary, the present approach, by combining the predictive power of computer-based database analysis with the establishment of robust, repeatable experimental conditions, proposes PCR-based RNA fingerprinting as a rejuvenated, efficient approach to the analysis of differential gene expression.

ACKNOWLEDGEMENTS
We thank Carol L. Stayton and Nicoletta Malgaretti for their essential contribution to the described protocols. This methodological work was made possible by grants from the Italian Telethon (B14) and the Associazione Italiana Sclerosi Multipla (AISM) to GGC and CNR target project on biotechnology to AC.
 

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Computation of Efficiency Index (E.I.) and Selectivity Index (S.I.).

    A modal value was computed from smooth lines fit to histograms of number of simulated PCR products per primer (see, e.g. red lines in figures 1A and 2A). The efficiency index, E.I., for each primer was computed as the decimal logarithm of the ratio:

Computation of the expected distribution of PCR products per primer

    Let the databank, D, be a set of N sequences, . Given that S_s is a sequence composed of a_s C/G nucleotides and b_s A/T nucleotides, the probability of a G or C nucleotide in the primer matching an arbitrary nucleotide in the sequence is , and the corresponding probability for A or T is  .

    In order to obtain hybridization a certain degree of matching must be obtained; here we arbitrarily decided that hybridization would occur for at least 9 matching bases out of 12, with no mismatches within the last 4 bases at the 3' end. Under these conditions, the probability of hybridization for a given template sequence and a given primer is a function of  the fraction of C/G nucleotides in the sequence, , the number of C/G in the first 8 bases of the primer, n₁, and the number of C/G in the last 4 bases at the 3' end, n₂.

    For any specific alignment of the primer on the template S_s, we have:
 

so that the probability of hybridization for any specific alignment of the primer on the template is P_s = A_s x B_s. The average value of F_s was 0.53 (* 0.082) and in general P_s was about 1-2x10^-4, its value increasing for primers with increasing numbers of C/G nucleotides in the last 4 positions.

    Assuming that PCR products of interest would have a length, L, comprized between L₀ = 100 and L₁ = 1000 BP, then the number of combinations of two acceptable positions on a template sequence S_s, of length M_s is:

 

    This gives rise to a binomial distribution of the number of PCR products obtained from template sequence S_s of length M_s and a primer with given values of n₁ and n₂. Such distribution is defined by the binomial parameters   and n = C_s; the corresponding probability density function (p.d.f.) is: .

    Actually, to estimate the probability of obtaining simulated PCR products (neglecting the technical aspects connected to experimentally obtaining a PCR amplification product), the unwanted possibility of a further hybridization in between the two valid positions must be excluded. For a product of length L, this possibility has an approximate probability of , and therefore about 900 x P_s for the average product length of 450 BP. For the usual magnitude of P_s this factor amounts to about 10^-2 and can be neglected.
The distributions of PCR products from the N sequences in the database   are expected to be independent. Therefore, the corresponding characteristic functions (ch.f.) can be computed   and the ch.f. for the whole databank will simply be: .

    From j_D(u) the expected p.d.f. of the number of PCR products from the whole databank, p_D(x), is computed for each primer. Averaging over the set of primers yields the expected distribution of the number of PCR products per primer (P₁).  Notice that p_D(x) is necessarily equal for primers having the same values of n₁, n₂ and P_s. Thus, P₁ may be multimodal (up to 5 peaks for n₂ = 0 to 4).

    The same procedure is used to compute the expected p.d.f. of the number of PCR products from each sequence, P₂ (in this case the ch.f. is computed by summing the logs of the single ch.f.'s over the set of primers for the same sequence).

    A third distribution of interest is that of the number of "successful" primers per sequence (i.e. yielding at least one PCR product from the sequence),  P₃. This is computed in the same way using the modified p.d.f., p_s, such that:  .
    Distribution P₁ is used to check whether the observed distribution of PCR products per primer significantly departs from the expectation: if a marked excess of particularly "good" and "poor" primers are found, this argues against a purely random distribution of nucleotides in the sequences of the databank.
Distributions P₂ and P₃ yield information on the exhaustivity of the approach, i.e. the capability of picking out as many different sequences as possible. In particular, the shape of the p.d.f. P₃ can be compared to the corresponding distribution, obtained by the simulation experiments, to check whether any bias is present towards a subpopulation of sequences (i.e. whether some sequences are significantly more subject to amplification than others).

    This approach cannot be straightforwardly applied to the distributions obtained using sets of particularly efficient primers. These distributions are obviously shifted to the right, with respect to the p.d.f. P₃, which is computed based on a purely random nucleotide composition of the databank. The size of the shift is conveniently represented by the ratio of the mean values. If the shift simply reflects an increased hybridization probability with no bias towards sequence subpopulations, the shape of the curve will be easily reproduced by computing the logarithm of the characteristic function of the expected probability, multiplying it by the ratio of the means and computing the resulting probability distribution (this is performed by applying the direct and inverse fast Fourier transforms). A reasonable agreement with the observed distribution will argue against biases in favor of specific sequence subpopulations.

FIGURE LEGENDS

Figure 1. (A) Histogram of the number of simulated CDS-containing PCR products per primer (503 primers tested on a human nonredundant cDNA database). Also shown is the expected distribution of product numbers per primer against a randomly scrambled sequence database (blue line). Notice the large excess of poorly efficient and highly efficient primers. (B) Scatter plot of the number of simulated PCR products in CDS, yielded by each primer when tested on the human (x-axis) or mouse (y-axis) cDNA databases. (C) Analysis of exhaustivity and redundancy on the results of simulated PCR (96 "best" primers - see text - tested on a human nonredundant cDNA database). Shown is the distribution of the number of simulated PCR products per transcript. The solid line represents the distribution of product numbers per transcript expected against a randomly scrambled sequence database. The dashed line represents the distribution expected after increasing the theoretical probability of matching by a factor equal to the ratio of observed to expected mean product numbers per transcript.

Figure 2. Efficiency and selectivity of pairs of dodecanucleotide primers containing a partially degenerate nucleotide at the 3' end (W or S), tested against a human nonredundant cDNA database. (A) histogram of the number of simulated PCR products per primer pair (solid line: 522 primer pairs; dashed line: 96 best pairs). (B) scatter plot of the selectivity index (SI) versus the efficiency Index (EI). See text and appendix for the meaning and computation of the two indices. The area enclosed in the square corresponds to EI's between 0.3 and 1.18 (i.e. 2 to 15 times the modal number of products) and SI's above 1.4. Primer pairs falling in this range were included in the optimal primer set, and ordered according to their SI's. The resulting primer set is reported in table 1. (C) distribution of the number of different degenerate primers yielding simulated PCR products from each transcript. Solid and dashed lines are the expected distributions before and after correcting the theoretical probability of matching as in figure 1C.

Figure 3. Relationship between redundancy (average number of simulated PCR products per transcript) and coverage (percentage of transcripts targeted by at least one primer). Simulations performed on human (Hu) and mouse (Mo) nonredundant sequence databases. All simulations  with 12-nt primers; 3 mismatches allowed, unless otherwise specified. Squares: observed coverage vs. redundancy in various simulations. (A,B) Hu, degenerate primers (max. 2 mismatches, 8 C's or G's), best 96 and all primers, respectively; (C) Mo, nondegenerate primers (6 C's or G's), 57 primers yielding >100 products; (D) Mo, nondegenerate primers (8 C's or G's), 93 primers yielding >100 products; (E) Hu, nondegenerate primers (6 C's or G's), 68 primers yielding >100 products; (F,G) Hu, nondegenerate primers (8 C's or G's), 96 best primers, and all primers, respectively; (H,I) Hu, degenerate primers (8 C's or G's), 96 best and all primers, respectively. Triangles: corresponding expected coverage, calculated for scrambled sequence databases; the observed numbers of failures are lower than expected in simulations performed with sets of selected primers (filled symbols). The dashed line represents the predictions of Poisson statistics. The continuous line is an arbitrary analytical function which suggests the following empirical relationship: coverage = 100 ´ (redundancy – 0.5) / (redundancy + 0.5) %.

Figure 4. (A) Diagram illustrating the mode of annealing of twelve-mer primers to known sequences deposited in nucleotide databases (GenBank, DBEST). Shown, is the percentage of perfect matches, as opposed to degenerate annealing, observed in 29 cases employing 8 different primers. y axis: percentages of nondegenerate annealing measured at each residue of the 29 primers analyzed. (B) Correlation between product numbers predicted by simulations and numbers of bands observed in RNA fingerprinting experiments performed with the corresponding primers. Simulations were performed on mouse nonredundant cDNA database, using 12-nt primers (8 C-G, 4 A-T), degenerate and nondegenerate. Thirteen primers were arbitrarily chosen among those yielding low, medium and high numbers of simulated PCR products; PCR experiments were performed as described in the Methods section, and only clearly discernible bands were recorded. Examples of RNA fingerprinting gels obtained with our method are published elsewhere (Malgaretti et al., 1997; Cabibbo et al., 1998)

TABLE LEGENDS

Table 1- 96 primers selected for their high efficiency in yielding large number of PCR products, expressed as efficiency index (E.I.), and for their selectivity for coding regions versus untranslated regions of transcripts, expressed as selectivity index (S.I.). See text and Appendix for the significance and computation of the two indices.

Table 2 - Transcript sizes (kb) and coding sequence (CDS) sizes (bp) of cDNAs deposited into the Genbank database, identified by PCR-based differential screening using our computer-assisted gene fishing approach.
 

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