Chips 99 Torrent 11
Semiconductor high-throughput sequencing, represented by Ion Torrent PGM/Proton, proves to be feasible in the noninvasive prenatal diagnosis of fetal aneuploidies. It is commendable that, with less data and relevant cost also, an accurate result can be achieved owing to the high sensitivity and specificity of such kind of technology. We conducted a comparative analysis of the performance of four different Ion chips in detecting fetal chromosomal aneuploidies. Eight maternal plasma DNA samples, including four pregnancies with normal fetuses and four with trisomy 21 fetuses, were sequenced on Ion Torrent 314/316/318/PI chips, respectively. Results such as read mapped ratio, correlation coefficient and phred quality score were calculated and parallelly compared. All samples were correctly classified even with low-throughput chip, and, among the four chips, the 316 chip had the highest read mapped ratio, correlation coefficient, mean read length and phred quality score. All chips were well consistent with each other. Our results showed that all Ion chips are applicable in noninvasive prenatal fetal aneuploidy diagnosis. We recommend researchers or clinicians to use the appropriate chip with barcoding technology on the basis of the sample number.
Chips 99 torrent 11
We selected maternal plasma DNA of eight pregnant women to perform sequencing analysis using the Ion 314/316/318 and PI chips. As the capacities of PGM chips are much smaller than those of the PI chips, we intended to test one sample on the 314/316/318 chips and several samples were pooled on a PI chip to be sequenced simultaneously; nearly 1G data were accessible for one sample.
Among these eight samples, four were normal and four were of trisomy 21. The karyotyping analysis results were shown in the Figure 1. All the samples were correctly classified. That is to say, even chips with low throughputs can provide significant results.
Different chips have different throughputs. Although the total reads that every chip can obtain varies, we can evaluate the quality of the chips by comparing the stability of the chips. Eight identical samples were sequenced by the four chips and the average read number of the eight samples sequenced by the corresponding chip was calculated. In addition, another statistic, the standard deviation of the read number divided by the average read number was also provided to show the stability of the four chips (Table 1). From the table, we can see that the 316 chip has the lowest value of s.d./average, which means that, among the eight samples sequenced, the total reads of the sample acquired by the 316 chip floated the least so that the 316 chip is the most stable.
The ion torrent PGM generates a great number of reads. How many of these reads can be mapped to the genome is also a factor for evaluating the chips. The results (Table 1) showed that all the four chips performed well and that, on average, more than 98% of the reads could be mapped to genome. However, the 316 chip has the highest average ratio of reads mapped to genome (99.197%), whereas the PI chip has the lowest standard deviation (0.10908).
The length of the reads contributes a lot to the mapping quality. As the read length grows, the number of reads that uniquely map to the genome also increases. Among the four chips, the 316 chip had the longest mean read length and the lowest standard deviation (Table 1).
We calculated the Q10, Q20 and Q30 of every sample, and, for every chip, we averaged them among the eight samples sequenced on the same chip. From the results, we found that the sequencing quality of the 316 chip was higher than that of the 314, 318 and PI chips, for 316 had the highest Q10 and Q20, whereas the Q30 was slightly lower than 318 (Table 1).
Correlation of chromosome 21 (chr21)% between chips. The percentage of chr21 sequenced by the 314, 316, 318 and PI chips was compared. This figure showed that the results from different chips were highly correlated.
Our study has already shown that even a chip with low throughput, such as the 314 chip, could also provide right results. Assuming the minimum data amount for trisomy diagnosis to be the throughput of 314 chips, namely 0.5M reads, theoretically, 6 samples can be pooled on a 316 chip, 12 samples on a 318 chip and 150 samples on a PI chip, respectively. However, as the quality control step may reduce the usable total reads and as more data amounts always ensure a lower false positive rate, pooling fewer samples than that theoretical value will be better.
In this paper, we performed a chip comparison study of Ion Torrent systems for noninvasive prenatal fetal aneuploidy diagnosis. Remarkably, Proton I was introduced to this study and compared with PGM. Through the output data we could see that the PI system was still not fully mature in its sequencing capability. The sequencing length on average was shorter than that of PGM and overall data quality was also not quite as good as that from PGM. In general, the 316 chip proved to be better than the others in most aspects, including longest mean read length, highest mapping rate, best correlation coefficient and highest Q10 and Q20. On the other hand, although the PI chip had the highest throughput, the sequencing quality still needed to be improved, especially for the read length and Q30, which were far behind the other three chips.
Although the throughputs varied among chips, all the samples were correctly classified as T21 or not. For quantitative study, such as noninvasive prenatal fetal aneuploidy diagnosis, the throughput seems not to be a challenge. Chip with low throughput, such as the 314 chip, can also give the right result. This enables us to perform such kind of diagnosis at a low cost.
In conclusion, all Ion chips are applicable in noninvasive prenatal fetal aneuploidy diagnosis, and we recommend researchers and clinicians to use appropriate chips with barcoding technology, based on the sample number.
The seminal importance of DNA sequencing to the life sciences, biotechnology and medicine has driven the search for more scalable and lower-cost solutions. Here we describe a DNA sequencing technology in which scalable, low-cost semiconductor manufacturing techniques are used to make an integrated circuit able to directly perform non-optical DNA sequencing of genomes. Sequence data are obtained by directly sensing the ions produced by template-directed DNA polymerase synthesis using all-natural nucleotides on this massively parallel semiconductor-sensing device or ion chip. The ion chip contains ion-sensitive, field-effect transistor-based sensors in perfect register with 1.2 million wells, which provide confinement and allow parallel, simultaneous detection of independent sequencing reactions. Use of the most widely used technology for constructing integrated circuits, the complementary metal-oxide semiconductor (CMOS) process, allows for low-cost, large-scale production and scaling of the device to higher densities and larger array sizes. We show the performance of the system by sequencing three bacterial genomes, its robustness and scalability by producing ion chips with up to 10 times as many sensors and sequencing a human genome.
Bacterial genome sequencing and signal processing was performed as described earlier. We succeeded in sequencing all three genomes fivefold to tenfold in individual runs using the small ion chip, covering 96.80% to 99.99% of each genome, with genome-wide consensus accuracies as high as 99.99% (Table 1 and Supplementary Fig. 14). Escherichia coli sequencing with three successively larger ion chips produced 46 to over 270 megabases of sequence (Table 1).
J.M.R. conceived the technology, supervised the project and wrote the manuscript with input from co-authors. K.J., M.J.M. and J.B. designed chips. J.F.D., M.A., D.L., J.W.M., J.F.S., E.N., M.S., X.M., A.B., T.A.C., M.H., I.B.S., B.R., J.S., E.F., M.S., J.A.F., K.J.M. and J.H.L. developed methods. M.D., J.T.B., M.E., J.H., N.H., T.M.R., B.P.P., S.E.C., M.L., Y.F. and A.W. wrote software and analysed data. W.H., J.S., W.M., D.M., J.R.N. and G.T.R. designed the instrument. E.D., D.D., R.K. and T.S. sequenced the human sample.
Progress towards cheaper and more compact DNA sequencing devices is limited by a number of factors, including the need for imaging technology. A new DNA sequencing technology that does away with optical readout, instead gathering sequence data by directly sensing hydrogen ions produced by template-directed DNA synthesis, offers a route to low cost and scalable sequencing on a massively parallel semiconductor-sensing device or ion chip. The reactions are performed using all natural nucleotides, and the individual ion-sensitive chips are disposable and inexpensive. The system has been used to sequence three bacterial genomes and a human genome: that of Gordon Moore of Moore's law fame.
We studied select valid variants by digital poymerase chain reaction (PCR) using the QuantStudio platform (ThermoFisher Scientific). For digital PCR, 2 chips were run per sample per assay on the QuantStudio 3D AnalysisSuite software. Digital PCR assays were custom synthesis and run with a no-template and a negative control as part of assay validation; positive controls of these mutations were not available. Further details are in Additional file 1: Table S1.
I like this stage as I have a special microfuge for my chips (see pic) and I get to make foam to help ensure efficient loading of the enriched, template-positive ISPs onto the chip. Finally, I flush the chip and add sequencing polymerase. The last step is to load the chip onto the proton and select the run required from my planned run list. The full protocol can be viewed here.
To test the toy data set, you can also run the following command from the SPAdes bin directory: spades.py --pe1-1 ../share/spades/test_dataset/ecoli_1K_1.fq.gz \ --pe1-2 ../share/spades/test_dataset/ecoli_1K_2.fq.gz -o spades_test If you have your library separated into several pairs of files, for example: lib1_forward_1.fastq lib1_reverse_1.fastq lib1_forward_2.fastq lib1_reverse_2.fastq make sure that corresponding files are given in the same order: spades.py --pe1-1 lib1_forward_1.fastq --pe1-2 lib1_reverse_1.fastq \ --pe1-1 lib1_forward_2.fastq --pe1-2 lib1_reverse_2.fastq \ -o spades_output Files with interlacing paired-end reads or files with unpaired reads can be specified in any order with one file per option, for example: spades.py --pe1-12 lib1_1.fastq --pe1-12 lib1_2.fastq \ --pe1-s lib1_unpaired_1.fastq --pe1-s lib1_unpaired_2.fastq \ -o spades_output If you have several paired-end and mate-pair reads, for example: paired-end library 1 lib_pe1_left.fastq lib_pe1_right.fastq mate-pair library 1 lib_mp1_left.fastq lib_mp1_right.fastq mate-pair library 2 lib_mp2_left.fastq lib_mp2_right.fastq make sure that files corresponding to each library are grouped together: spades.py --pe1-1 lib_pe1_left.fastq --pe1-2 lib_pe1_right.fastq \ --mp1-1 lib_mp1_left.fastq --mp1-2 lib_mp1_right.fastq \ --mp2-1 lib_mp2_left.fastq --mp2-2 lib_mp2_right.fastq \ -o spades_output If you have IonTorrent unpaired reads, PacBio CLR and additional reliable contigs: it_reads.fastq pacbio_clr.fastq contigs.fasta run SPAdes with the following command: spades.py --iontorrent -s it_reads.fastq \ --pacbio pacbio_clr.fastq --trusted-contigs contigs.fastq \ -o spades_output If a single-read library is splitted into several files: unpaired1_1.fastq unpaired1_2.fastq unpaired1_3.fasta specify them as one library: spades.py --s1 unpaired1_1.fastq \ --s1 unpaired1_2.fastq --s1 unpaired1_3.fastq \ -o spades_output All options for specifying input data can be mixed if needed, but make sure that files for each library are grouped and files with left and right paired reads are listed in the same order.3.3 Assembling IonTorrent readsOnly FASTQ or BAM files are supported as input.The selection of k-mer length is non-trivial for IonTorrent. If the dataset is more or less conventional (good coverage, not high GC, etc), then use our recommendation for long reads (e.g. assemble using k-mer lengths 21,33,55,77,99,127). However, due to increased error rate some changes of k-mer lengths (e.g. selection of shorter ones) may be required. For example, if you ran SPAdes with k-mer lengths 21,33,55,77 and then decided to assemble the same data set using more iterations and larger values of K, you can run SPAdes once again specifying the same output folder and the following options: --restart-from k77 -k 21,33,55,77,99,127 --mismatch-correction -o . Do not forget to copy contigs and scaffolds from the previous run. We're planning to tackle issue of selecting k-mer lengths for IonTorrent reads in next versions. You may need no error correction for Hi-Q enzyme at all. However, we suggest trying to assemble your data with and without error correction and select the best variant. For non-trivial datasets (e.g. with high GC, low or uneven coverage) we suggest to enable single-cell mode (setting --sc option) and use k-mer lengths of 21,33,55.3.4 Assembling long Illumina paired reads (2x150 and 2x250)Recent advances in DNA sequencing technology have led to a rapid increase in read length. Nowadays, it is a common situation to have a data set consisting of 2x150 or 2x250 paired-end reads produced by Illumina MiSeq or HiSeq2500. However, the use of longer reads alone will not automatically improve assembly quality. An assembler that can properly take advantage of them is needed.SPAdes' use of iterative k-mer lengths allows benefiting from the full potential of the long paired-end reads. Currently one has to set the assembler options up manually, but we plan to incorporate automatic calculation of necessary options soon.Please note that in addition to the read length, the insert length also matters a lot. It is not recommended to sequence a 300bp fragment with a pair of 250bp reads. We suggest using 350-500 bp fragments with 2x150 reads and 550-700 bp fragments with 2x250 reads.Multi-cell data set with read length 2x150Do not turn off SPAdes error correction (BayesHammer module), which is included in SPAdes default pipeline.If you have enough coverage (50x+), then you may want to try to set k-mer lengths of 21, 33, 55, 77 (selected by default for reads with length 150bp).Make sure you run assembler with the --careful option to minimize number of mismatches in the final contigs.We recommend that you check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.For reads corrected prior to running the assembler: spades.py -k 21,33,55,77 --careful --only-assembler -o spades_output To correct and assemble the reads: spades.py -k 21,33,55,77 --careful -o spades_outputMulti-cell data set with read lengths 2 x 250Do not turn off SPAdes error correction (BayesHammer module), which is included in SPAdes default pipeline.By default we suggest to increase k-mer lengths in increments of 22 until the k-mer length reaches 127. The exact length of the k-mer depends on the coverage: k-mer length of 127 corresponds to 50x k-mer coverage and higher. For read length 250bp SPAdes automatically chooses K values equal to 21, 33, 55, 77, 99, 127.Make sure you run assembler with --careful option to minimize number of mismatches in the final contigs.We recommend you to check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.For reads corrected prior to running the assembler: spades.py -k 21,33,55,77,99,127 --careful --only-assembler -o spades_output To correct and assemble the reads: spades.py -k 21,33,55,77,99,127 --careful -o spades_outputSingle-cell data set with read lengths 2 x 150 or 2 x 250The default k-mer lengths are recommended. For single-cell data sets SPAdes selects k-mer sizes 21, 33 and 55.However, it might be tricky to fully utilize the advantages of long reads you have. Consider contacting us for more information and to discuss assembly strategy.3.5 SPAdes output SPAdes stores all output files in , which is set by the user. /corrected/ directory contains reads corrected by BayesHammer in *.fastq.gz files; if compression is disabled, reads are stored in uncompressed *.fastq files
/scaffolds.fasta contains resulting scaffolds (recommended for use as resulting sequences)
/contigs.fasta contains resulting contigs
/assembly_graph.gfa contains SPAdes assembly graph and scaffolds paths in GFA 1.0 format
/assembly_graph.fastg contains SPAdes assembly graph in FASTG format
/contigs.paths contains paths in the assembly graph corresponding to contigs.fasta (see details below)
/scaffolds.paths contains paths in the assembly graph corresponding to scaffolds.fasta (see details below)
Contigs/scaffolds names in SPAdes output FASTA files have the following format: >NODE_3_length_237403_cov_243.207_ID_45 Here 3 is the number of the contig/scaffold, 237403 is the sequence length in nucleotides and 243.207 is the k-mer coverage for the last (largest) k value used. Note that the k-mer coverage is always lower than the read (per-base) coverage. In general, SPAdes uses two techniques for joining contigs into scaffolds. First one relies on read pairs and tries to estimate the size of the gap separating contigs. The second one relies on the assembly graph: e.g. if two contigs are separated by a complex tandem repeat, that cannot be resolved exactly, contigs are joined into scaffold with a fixed gap size of 100 bp. Contigs produced by SPAdes do not contain N symbols. To view FASTG and GFA files we recommend to use Bandage visualization tool. Note that sequences stored in assembly_graph.fastg correspond to contigs before repeat resolution (edges of the assembly graph). Paths corresponding to contigs after repeat resolution (scaffolding) are stored in contigs.paths (scaffolds.paths) in the format accepted by Bandage (see Bandage wiki for details). The example is given below. Let the contig with the name NODE_5_length_100000_cov_215.651_ID_5 consist of the following edges of the assembly graph: >EDGE_2_length_33280_cov_199.702 >EDGE_5_length_84_cov_321.414' >EDGE_3_length_111_cov_175.304 >EDGE_5_length_84_cov_321.414' >EDGE_4_length_66661_cov_223.548 Then, contigs.paths will contain the following record: NODE_5_length_100000_cov_215.651_ID_5 2+,5-,3+,5-,4+ Since the current version of Bandage does not accept paths with gaps, paths corresponding contigs/scaffolds jumping over a gap in the assembly graph are splitted by semicolon at the gap positions. For example, the following record NODE_3_length_237403_cov_243.207_ID_45 21-,17-,15+,17-,16+; 31+,23-,22+,23-,4- states that NODE_3_length_237403_cov_243.207_ID_45 corresponds to the path with 10 edges, but jumps over a gap between edges EDGE_16_length_21503_cov_482.709 and EDGE_31_length_140767_cov_220.239.The full list of content is presented below: