Ontology type: schema:ScholarlyArticle Open Access: True
2018-01-26
AUTHORSGarold Fuks, Michael Elgart, Amnon Amir, Amit Zeisel, Peter J. Turnbaugh, Yoav Soen, Noam Shental
ABSTRACTBackgroundMost of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16–33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.ResultsThis manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis.ConclusionsSMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST). More... »
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39 | ″ | ″ | BackgroundMost |
40 | ″ | ″ | Computationally |
41 | ″ | ″ | DNA |
42 | ″ | ″ | Earth |
43 | ″ | ″ | Human Microbiome Project |
44 | ″ | ″ | Microbiome Project |
45 | ″ | ″ | PCR |
46 | ″ | ″ | amplicon length |
47 | ″ | ″ | amplicons |
48 | ″ | ″ | analysis |
49 | ″ | ″ | approach |
50 | ″ | ″ | bacteria |
51 | ″ | ″ | bacterial biomass |
52 | ″ | ″ | bacterial isolates |
53 | ″ | ″ | biological samples |
54 | ″ | ″ | biomass |
55 | ″ | ″ | cases |
56 | ″ | ″ | cases of formalin |
57 | ″ | ″ | community |
58 | ″ | ″ | community profiling |
59 | ″ | ″ | computational framework |
60 | ″ | ″ | conditions |
61 | ″ | ″ | convex optimization problem |
62 | ″ | ″ | coverage |
63 | ″ | ″ | current techniques |
64 | ″ | ″ | custom set |
65 | ″ | ″ | depth |
66 | ″ | ″ | different PCR |
67 | ″ | ″ | different ways |
68 | ″ | ″ | diversity |
69 | ″ | ″ | downstream analysis |
70 | ″ | ″ | efficient downstream analysis |
71 | ″ | ″ | fast reconstruction |
72 | ″ | ″ | formalin |
73 | ″ | ″ | fragmented DNA |
74 | ″ | ″ | framework |
75 | ″ | ″ | gene sequences |
76 | ″ | ″ | genes |
77 | ″ | ″ | gut bacterial isolates |
78 | ″ | ″ | harsh conditions |
79 | ″ | ″ | high resolution |
80 | ″ | ″ | high-resolution profiling |
81 | ″ | ″ | identification |
82 | ″ | ″ | improvement |
83 | ″ | ″ | increase |
84 | ″ | ″ | independent regions |
85 | ″ | ″ | isolates |
86 | ″ | ″ | knowledge |
87 | ″ | ″ | large coverage |
88 | ″ | ″ | length |
89 | ″ | ″ | little modification |
90 | ″ | ″ | low bacterial biomass |
91 | ″ | ″ | manuscript |
92 | ″ | ″ | memory |
93 | ″ | ″ | method |
94 | ″ | ″ | microbial communities |
95 | ″ | ″ | microbial community profiling |
96 | ″ | ″ | microbial diversity |
97 | ″ | ″ | microbiome |
98 | ″ | ″ | mixture |
99 | ″ | ″ | mock mixtures |
100 | ″ | ″ | moderate memory |
101 | ″ | ″ | modification |
102 | ″ | ″ | multilocus sequence typing |
103 | ″ | ″ | need |
104 | ″ | ″ | next-generation sequencing methods |
105 | ″ | ″ | number |
106 | ″ | ″ | optimization problem |
107 | ″ | ″ | pairs |
108 | ″ | ″ | paraffin-embedded samples |
109 | ″ | ″ | platform |
110 | ″ | ″ | platforms today |
111 | ″ | ″ | preparation protocol |
112 | ″ | ″ | primer pairs |
113 | ″ | ″ | problem |
114 | ″ | ″ | profiling |
115 | ″ | ″ | profiling approach |
116 | ″ | ″ | project |
117 | ″ | ″ | protocol |
118 | ″ | ″ | rRNA gene |
119 | ″ | ″ | rRNA gene sequences |
120 | ″ | ″ | rRNA gene variable regions |
121 | ″ | ″ | read length |
122 | ″ | ″ | real-world biological samples |
123 | ″ | ″ | reconstruction |
124 | ″ | ″ | region |
125 | ″ | ″ | region framework |
126 | ″ | ″ | remarkable microbial diversity |
127 | ″ | ″ | researchers |
128 | ″ | ″ | resolution |
129 | ″ | ″ | results |
130 | ″ | ″ | sample number |
131 | ″ | ″ | sample preparation protocol |
132 | ″ | ″ | samples |
133 | ″ | ″ | sequence |
134 | ″ | ″ | sequence typing |
135 | ″ | ″ | sequencing approach |
136 | ″ | ″ | sequencing depth |
137 | ″ | ″ | sequencing methods |
138 | ″ | ″ | sequencing results |
139 | ″ | ″ | set |
140 | ″ | ″ | set of amplicons |
141 | ″ | ″ | shortcomings |
142 | ″ | ″ | shotgun sequencing approach |
143 | ″ | ″ | silico simulations |
144 | ″ | ″ | simulations |
145 | ″ | ″ | single region |
146 | ″ | ″ | standard sample preparation protocols |
147 | ″ | ″ | subset |
148 | ″ | ″ | technique |
149 | ″ | ″ | today |
150 | ″ | ″ | total length |
151 | ″ | ″ | twofold improvement |
152 | ″ | ″ | typing |
153 | ″ | ″ | use |
154 | ″ | ″ | variable regions |
155 | ″ | ″ | way |
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